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[FreeCourseSite.com] Udemy - TensorFlow Developer Certificate in 2022 Zero to Mastery
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10 - NLP Fundamentals in TensorFlow
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016 Visualising our model's learned word embeddings with TensorFlow's projector tool.mp4 - 286.86 MB
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015 Model 1 Building, fitting and evaluating our first deep model on text data.mp4 - 205.35 MB
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009 Setting up a TensorFlow TextVectorization layer to convert text to numbers.mp4 - 199.84 MB
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021 Discussing the intuition behind Conv1D neural networks for text and sequences.mp4 - 181.04 MB
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019 Model 3 Building, fitting and evaluating a GRU-cell powered RNN.mp4 - 165.96 MB
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027 Fixing our data leakage issue with model 7 and retraining it.mp4 - 165.47 MB
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020 Model 4 Building, fitting and evaluating a bidirectional RNN model.mp4 - 162.79 MB
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018 Model 2 Building, fitting and evaluating our first TensorFlow RNN model (LSTM).mp4 - 162.44 MB
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006 Becoming one with the data and visualising a text dataset.mp4 - 158.24 MB
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014 Creating a function to track and evaluate our model's results.mp4 - 149 MB
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023 Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP).mp4 - 136.58 MB
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011 Creating an Embedding layer to turn tokenised text into embedding vectors.mp4 - 134.58 MB
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031 Downloading a pretrained model and preparing data to investigate predictions.mp4 - 128.9 MB
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002 Introduction to Natural Language Processing (NLP) and Sequence Problems.mp4 - 122.09 MB
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028 Comparing all our modelling experiments evaluation metrics.mp4 - 113.64 MB
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034 Understanding the concept of the speedscore tradeoff.mp4 - 109.2 MB
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029 Uploading our model's training logs to TensorBoard and comparing them.mp4 - 107.3 MB
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004 The typical architecture of a Recurrent Neural Network (RNN).mp4 - 106.75 MB
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026 Model 7 Building, training and evaluating a transfer learning model on 10% data.mp4 - 99.89 MB
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024 Model 6 Building, training and evaluating a transfer learning model for NLP.mp4 - 97.15 MB
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010 Mapping the TextVectorization layer to text data and turning it into numbers.mp4 - 95.55 MB
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017 High-level overview of Recurrent Neural Networks (RNNs) + where to learn more.mp4 - 95.36 MB
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013 Model 0 Building a baseline model to try and improve upon.mp4 - 92.07 MB
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025 Preparing subsets of data for model 7 (same as model 6 but 10% of data).mp4 - 89.06 MB
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030 Saving and loading in a trained NLP model with TensorFlow.mp4 - 86.89 MB
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012 Discussing the various modelling experiments we're going to run.mp4 - 86.12 MB
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005 Preparing a notebook for our first NLP with TensorFlow project.mp4 - 81.3 MB
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008 Converting text data to numbers using tokenisation and embeddings (overview).mp4 - 80.32 MB
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032 Visualising our model's most wrong predictions.mp4 - 75.63 MB
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022 Model 5 Building, fitting and evaluating a 1D CNN for text.mp4 - 75.3 MB
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033 Making and visualising predictions on the test dataset.mp4 - 75.21 MB
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007 Splitting data into training and validation sets.mp4 - 58.91 MB
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003 Example NLP inputs and outputs.mp4 - 27.29 MB
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016 Visualising our model's learned word embeddings with TensorFlow's projector tool_en.srt - 29.71 KB
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015 Model 1 Building, fitting and evaluating our first deep model on text data_en.srt - 28.65 KB
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020 Model 4 Building, fitting and evaluating a bidirectional RNN model_en.srt - 27.18 KB
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021 Discussing the intuition behind Conv1D neural networks for text and sequences_en.srt - 26.93 KB
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018 Model 2 Building, fitting and evaluating our first TensorFlow RNN model (LSTM)_en.srt - 24.61 KB
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019 Model 3 Building, fitting and evaluating a GRU-cell powered RNN_en.srt - 23.85 KB
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006 Becoming one with the data and visualising a text dataset_en.srt - 22.19 KB
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009 Setting up a TensorFlow TextVectorization layer to convert text to numbers_en.srt - 22.18 KB
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002 Introduction to Natural Language Processing (NLP) and Sequence Problems_en.srt - 20.24 KB
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023 Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP)_en.srt - 19.47 KB
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034 Understanding the concept of the speedscore tradeoff_en.srt - 18.65 KB
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011 Creating an Embedding layer to turn tokenised text into embedding vectors_en.srt - 17.91 KB
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028 Comparing all our modelling experiments evaluation metrics_en.srt - 17.88 KB
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027 Fixing our data leakage issue with model 7 and retraining it_en.srt - 17.27 KB
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014 Creating a function to track and evaluate our model's results_en.srt - 16.71 KB
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031 Downloading a pretrained model and preparing data to investigate predictions_en.srt - 16.48 KB
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010 Mapping the TextVectorization layer to text data and turning it into numbers_en.srt - 15.9 KB
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029 Uploading our model's training logs to TensorBoard and comparing them_en.srt - 15.33 KB
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025 Preparing subsets of data for model 7 (same as model 6 but 10% of data)_en.srt - 15.31 KB
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024 Model 6 Building, training and evaluating a transfer learning model for NLP_en.srt - 15.09 KB
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022 Model 5 Building, fitting and evaluating a 1D CNN for text_en.srt - 14.82 KB
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012 Discussing the various modelling experiments we're going to run_en.srt - 13.77 KB
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017 High-level overview of Recurrent Neural Networks (RNNs) + where to learn more_en.srt - 13.76 KB
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030 Saving and loading in a trained NLP model with TensorFlow_en.srt - 13.54 KB
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004 The typical architecture of a Recurrent Neural Network (RNN)_en.srt - 13.41 KB
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008 Converting text data to numbers using tokenisation and embeddings (overview)_en.srt - 13.09 KB
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026 Model 7 Building, training and evaluating a transfer learning model on 10% data_en.srt - 12.88 KB
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013 Model 0 Building a baseline model to try and improve upon_en.srt - 12.63 KB
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032 Visualising our model's most wrong predictions_en.srt - 12.28 KB
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005 Preparing a notebook for our first NLP with TensorFlow project_en.srt - 11.73 KB
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003 Example NLP inputs and outputs_en.srt - 11.68 KB
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033 Making and visualising predictions on the test dataset_en.srt - 11.62 KB
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007 Splitting data into training and validation sets_en.srt - 7.86 KB
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035 NLP Fundamentals in TensorFlow challenge, exercises and extra-curriculum.html - 2.14 KB
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001 Welcome to natural language processing with TensorFlow!.html - 1.03 KB
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07 - Transfer Learning in TensorFlow Part 2 Fine tuning
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019 Preparing Model 3 (our first fine-tuned model).mp4 - 194.01 MB
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003 Downloading and turning our images into a TensorFlow BatchDataset.mp4 - 172.23 MB
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015 Building Model 2 (with a data augmentation layer and 10% of training data).mp4 - 156.72 MB
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014 Building Model 1 (with a data augmentation layer and 1% of training data).mp4 - 150.29 MB
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008 Getting a feature vector from our trained model.mp4 - 146.78 MB
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007 Compiling and fitting our first Functional API model.mp4 - 133.62 MB
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006 Creating our first model with the TensorFlow Keras Functional API.mp4 - 131.54 MB
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013 Visualizing what happens when images pass through our data augmentation layer.mp4 - 120.27 MB
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011 Building a data augmentation layer to use inside our model.mp4 - 115.83 MB
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024 Fine-tuning Model 4 on 100% of the training data and evaluating its results.mp4 - 95.34 MB
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025 Comparing our modelling experiment results in TensorBoard.mp4 - 93.28 MB
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023 Preparing our final modelling experiment (Model 4).mp4 - 93.11 MB
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002 Importing a script full of helper functions (and saving lots of space).mp4 - 89.59 MB
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010 Downloading and preparing the data for Model 1 (1 percent of training data).mp4 - 81.49 MB
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021 Comparing our model's results before and after fine-tuning.mp4 - 81.4 MB
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016 Creating a ModelCheckpoint to save our model's weights during training.mp4 - 67.78 MB
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001 Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning.mp4 - 60.83 MB
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020 Fitting and evaluating Model 3 (our first fine-tuned model).mp4 - 57.93 MB
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022 Downloading and preparing data for our biggest experiment yet (Model 4).mp4 - 55.42 MB
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009 Drilling into the concept of a feature vector (a learned representation).mp4 - 52.29 MB
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017 Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).mp4 - 44.07 MB
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018 Loading and comparing saved weights to our existing trained Model 2.mp4 - 41.08 MB
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026 How to view and delete previous TensorBoard experiments.mp4 - 18.31 MB
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005 Comparing the TensorFlow Keras Sequential API versus the Functional API.mp4 - 16.64 MB
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004 Discussing the four (actually five) modelling experiments we're running.mp4 - 7.69 MB
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019 Preparing Model 3 (our first fine-tuned model)_en.srt - 25.91 KB
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015 Building Model 2 (with a data augmentation layer and 10% of training data)_en.srt - 23.45 KB
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014 Building Model 1 (with a data augmentation layer and 1% of training data)_en.srt - 22.42 KB
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003 Downloading and turning our images into a TensorFlow BatchDataset_en.srt - 22.02 KB
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008 Getting a feature vector from our trained model_en.srt - 17.74 KB
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011 Building a data augmentation layer to use inside our model_en.srt - 16.15 KB
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013 Visualizing what happens when images pass through our data augmentation layer_en.srt - 16.1 KB
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006 Creating our first model with the TensorFlow Keras Functional API_en.srt - 15.85 KB
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007 Compiling and fitting our first Functional API model_en.srt - 15.77 KB
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025 Comparing our modelling experiment results in TensorBoard_en.srt - 15.74 KB
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023 Preparing our final modelling experiment (Model 4)_en.srt - 14.88 KB
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024 Fine-tuning Model 4 on 100% of the training data and evaluating its results_en.srt - 14.85 KB
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021 Comparing our model's results before and after fine-tuning_en.srt - 13.82 KB
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010 Downloading and preparing the data for Model 1 (1 percent of training data)_en.srt - 12.98 KB
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016 Creating a ModelCheckpoint to save our model's weights during training_en.srt - 10.72 KB
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020 Fitting and evaluating Model 3 (our first fine-tuned model)_en.srt - 10.61 KB
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017 Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint)_en.srt - 9.85 KB
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001 Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning_en.srt - 9.78 KB
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002 Importing a script full of helper functions (and saving lots of space)_en.srt - 9.77 KB
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018 Loading and comparing saved weights to our existing trained Model 2_en.srt - 9.65 KB
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022 Downloading and preparing data for our biggest experiment yet (Model 4)_en.srt - 8.97 KB
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009 Drilling into the concept of a feature vector (a learned representation)_en.srt - 5.39 KB
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005 Comparing the TensorFlow Keras Sequential API versus the Functional API_en.srt - 4.04 KB
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004 Discussing the four (actually five) modelling experiments we're running_en.srt - 3.58 KB
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026 How to view and delete previous TensorBoard experiments_en.srt - 2.81 KB
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027 Transfer Learning in TensorFlow Part 2 challenge, exercises and extra-curriculum.html - 2.61 KB
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012 Note Small fix for next video, for images not augmenting.html - 1.95 KB
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03 - Neural network regression with TensorFlow
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012 Evaluating a TensorFlow model part 3 (getting a model summary).mp4 - 192.53 MB
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006 The major steps in modelling with TensorFlow.mp4 - 180 MB
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026 Putting together what we've learned part 3 (improving our regression model).mp4 - 153.09 MB
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024 Putting together what we've learned part 1 (preparing a dataset).mp4 - 142.43 MB
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009 Steps in improving a model with TensorFlow part 3.mp4 - 131.87 MB
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018 Setting up TensorFlow modelling experiments part 1 (start with a simple model).mp4 - 124.97 MB
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025 Putting together what we've learned part 2 (building a regression model).mp4 - 100.75 MB
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019 Setting up TensorFlow modelling experiments part 2 (increasing complexity).mp4 - 93.44 MB
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021 How to save a TensorFlow model.mp4 - 91.74 MB
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027 Preprocessing data with feature scaling part 1 (what is feature scaling).mp4 - 91.31 MB
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020 Comparing and tracking your TensorFlow modelling experiments.mp4 - 90.38 MB
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004 Creating sample regression data (so we can model it).mp4 - 86.78 MB
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022 How to load and use a saved TensorFlow model.mp4 - 86.5 MB
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028 Preprocessing data with feature scaling part 2 (normalising our data).mp4 - 80.99 MB
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011 Evaluating a TensorFlow model part 2 (the three datasets).mp4 - 78.86 MB
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014 Evaluating a TensorFlow model part 5 (visualising a model's predictions).mp4 - 77.06 MB
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008 Steps in improving a model with TensorFlow part 2.mp4 - 74.62 MB
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029 Preprocessing data with feature scaling part 3 (fitting a model on scaled data).mp4 - 74.48 MB
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013 Evaluating a TensorFlow model part 4 (visualising a model's layers).mp4 - 69.39 MB
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015 Evaluating a TensorFlow model part 6 (common regression evaluation metrics).mp4 - 69.13 MB
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023 (Optional) How to save and download files from Google Colab.mp4 - 67.45 MB
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003 Anatomy and architecture of a neural network regression model.mp4 - 50.49 MB
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001 Introduction to Neural Network Regression with TensorFlow.mp4 - 50.04 MB
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016 Evaluating a TensorFlow regression model part 7 (mean absolute error).mp4 - 46.16 MB
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002 Inputs and outputs of a neural network regression model.mp4 - 39.69 MB
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007 Steps in improving a model with TensorFlow part 1.mp4 - 38.58 MB
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017 Evaluating a TensorFlow regression model part 7 (mean square error).mp4 - 31.93 MB
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010 Evaluating a TensorFlow model part 1 (visualise, visualise, visualise).mp4 - 27.62 MB
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006 The major steps in modelling with TensorFlow_en.srt - 26.45 KB
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012 Evaluating a TensorFlow model part 3 (getting a model summary)_en.srt - 21.53 KB
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026 Putting together what we've learned part 3 (improving our regression model)_en.srt - 18.8 KB
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024 Putting together what we've learned part 1 (preparing a dataset)_en.srt - 18.7 KB
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025 Putting together what we've learned part 2 (building a regression model)_en.srt - 17.95 KB
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018 Setting up TensorFlow modelling experiments part 1 (start with a simple model)_en.srt - 17.44 KB
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009 Steps in improving a model with TensorFlow part 3_en.srt - 16.84 KB
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004 Creating sample regression data (so we can model it)_en.srt - 16.12 KB
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019 Setting up TensorFlow modelling experiments part 2 (increasing complexity)_en.srt - 15.86 KB
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011 Evaluating a TensorFlow model part 2 (the three datasets)_en.srt - 14.05 KB
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028 Preprocessing data with feature scaling part 2 (normalising our data)_en.srt - 13.93 KB
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027 Preprocessing data with feature scaling part 1 (what is feature scaling)_en.srt - 13.88 KB
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020 Comparing and tracking your TensorFlow modelling experiments_en.srt - 13.18 KB
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002 Inputs and outputs of a neural network regression model_en.srt - 13.12 KB
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008 Steps in improving a model with TensorFlow part 2_en.srt - 13.12 KB
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022 How to load and use a saved TensorFlow model_en.srt - 12.82 KB
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003 Anatomy and architecture of a neural network regression model_en.srt - 12.25 KB
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014 Evaluating a TensorFlow model part 5 (visualising a model's predictions)_en.srt - 11.92 KB
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001 Introduction to Neural Network Regression with TensorFlow_en.srt - 11.41 KB
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021 How to save a TensorFlow model_en.srt - 11.4 KB
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015 Evaluating a TensorFlow model part 6 (common regression evaluation metrics)_en.srt - 11.16 KB
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029 Preprocessing data with feature scaling part 3 (fitting a model on scaled data)_en.srt - 10.97 KB
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010 Evaluating a TensorFlow model part 1 (visualise, visualise, visualise)_en.srt - 9.77 KB
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013 Evaluating a TensorFlow model part 4 (visualising a model's layers)_en.srt - 9.23 KB
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016 Evaluating a TensorFlow regression model part 7 (mean absolute error)_en.srt - 8.1 KB
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023 (Optional) How to save and download files from Google Colab_en.srt - 7.79 KB
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007 Steps in improving a model with TensorFlow part 1_en.srt - 7.63 KB
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017 Evaluating a TensorFlow regression model part 7 (mean square error)_en.srt - 3.89 KB
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005 Note Code update for upcoming lecture(s) for TensorFlow 2.7.0+ fix.html - 2.38 KB
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030 TensorFlow Regression challenge, exercises & extra-curriculum.html - 1.96 KB
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031 Learning Guideline.html - 336 B
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external-assets-links.txt - 130 B
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11 - Milestone Project 2 SkimLit
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017 Creating a character-level tokeniser with TensorFlow's TextVectorization layer.mp4 - 191.48 MB
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021 Model 4 Building a multi-input model (hybrid token + character embeddings).mp4 - 180.87 MB
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014 Model 1 Building, fitting and evaluating a Conv1D with token embeddings.mp4 - 166 MB
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029 Model 5 Completing the build of a tribrid embedding model for sequences.mp4 - 151.2 MB
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001 Introduction to Milestone Project 2 SkimLit.mp4 - 146.86 MB
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004 Setting up our notebook for Milestone Project 2 (getting the data).mp4 - 143.58 MB
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006 Writing a preprocessing function to structure our data for modelling.mp4 - 138.61 MB
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024 Model 4 Building, fitting and evaluating a hybrid embedding model.mp4 - 137.4 MB
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035 Congratulations and your challenge before heading to the next module.mp4 - 134.82 MB
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005 Visualising examples from the dataset (becoming one with the data).mp4 - 130.32 MB
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019 Model 3 Building, fitting and evaluating a Conv1D model on character embeddings.mp4 - 128.46 MB
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011 Creating a text vectoriser to map our tokens (text) to numbers.mp4 - 126.98 MB
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015 Preparing a pretrained embedding layer from TensorFlow Hub for Model 2.mp4 - 124.11 MB
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032 Bringing SkimLit to life!!! (fitting and evaluating Model 5).mp4 - 114.93 MB
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008 Turning our target labels into numbers (ML models require numbers).mp4 - 114.84 MB
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026 Encoding the line number feature to used with Model 5.mp4 - 110.36 MB
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030 Visually inspecting the architecture of our tribrid embedding model.mp4 - 106.96 MB
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012 Creating a custom token embedding layer with TensorFlow.mp4 - 98.86 MB
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031 Creating multi-level data input pipelines for Model 5 with the tf.data API.mp4 - 98.82 MB
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022 Model 4 Plotting and visually exploring different data inputs.mp4 - 86.22 MB
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010 Preparing our data for deep sequence models.mp4 - 83.32 MB
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023 Crafting multi-input fast loading tf.data datasets for Model 4.mp4 - 82.06 MB
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009 Model 0 Creating, fitting and evaluating a baseline model for SkimLit.mp4 - 79.53 MB
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013 Creating fast loading dataset with the TensorFlow tf.data API.mp4 - 76.12 MB
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033 Comparing the performance of all of our modelling experiments.mp4 - 75.64 MB
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007 Performing visual data analysis on our preprocessed text.mp4 - 73.09 MB
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002 What we're going to cover in Milestone Project 2 (NLP for medical abstracts).mp4 - 70.02 MB
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034 Saving, loading & testing our best performing model.mp4 - 69.28 MB
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028 Model 5 Building the foundations of a tribrid embedding model.mp4 - 68.71 MB
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003 SkimLit inputs and outputs.mp4 - 65.65 MB
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018 Creating a character-level embedding layer with tf.keras.layers.Embedding.mp4 - 64.72 MB
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027 Encoding the total lines feature to be used with Model 5.mp4 - 62.63 MB
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020 Discussing how we're going to build Model 4 (character + token embeddings).mp4 - 50.42 MB
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016 Model 2 Building, fitting and evaluating a Conv1D model with token embeddings.mp4 - 46.36 MB
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025 Model 5 Adding positional embeddings via feature engineering (overview).mp4 - 43.84 MB
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017 Creating a character-level tokeniser with TensorFlow's TextVectorization layer_en.srt - 29.76 KB
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006 Writing a preprocessing function to structure our data for modelling_en.srt - 25.97 KB
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014 Model 1 Building, fitting and evaluating a Conv1D with token embeddings_en.srt - 24.65 KB
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021 Model 4 Building a multi-input model (hybrid token + character embeddings)_en.srt - 22.59 KB
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001 Introduction to Milestone Project 2 SkimLit_en.srt - 22.02 KB
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004 Setting up our notebook for Milestone Project 2 (getting the data)_en.srt - 19.69 KB
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011 Creating a text vectoriser to map our tokens (text) to numbers_en.srt - 19.03 KB
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019 Model 3 Building, fitting and evaluating a Conv1D model on character embeddings_en.srt - 18.98 KB
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008 Turning our target labels into numbers (ML models require numbers)_en.srt - 18.82 KB
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024 Model 4 Building, fitting and evaluating a hybrid embedding model_en.srt - 18.6 KB
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029 Model 5 Completing the build of a tribrid embedding model for sequences_en.srt - 18.15 KB
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003 SkimLit inputs and outputs_en.srt - 18.09 KB
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035 Congratulations and your challenge before heading to the next module_en.srt - 17.19 KB
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005 Visualising examples from the dataset (becoming one with the data)_en.srt - 17.16 KB
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026 Encoding the line number feature to used with Model 5_en.srt - 16.66 KB
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016 Model 2 Building, fitting and evaluating a Conv1D model with token embeddings_en.srt - 16.1 KB
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015 Preparing a pretrained embedding layer from TensorFlow Hub for Model 2_en.srt - 14.99 KB
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032 Bringing SkimLit to life!!! (fitting and evaluating Model 5)_en.srt - 14.87 KB
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030 Visually inspecting the architecture of our tribrid embedding model_en.srt - 13.82 KB
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010 Preparing our data for deep sequence models_en.srt - 12.97 KB
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013 Creating fast loading dataset with the TensorFlow tf.data API_en.srt - 12.81 KB
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012 Creating a custom token embedding layer with TensorFlow_en.srt - 12.64 KB
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033 Comparing the performance of all of our modelling experiments_en.srt - 12.39 KB
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022 Model 4 Plotting and visually exploring different data inputs_en.srt - 12.31 KB
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002 What we're going to cover in Milestone Project 2 (NLP for medical abstracts)_en.srt - 11.87 KB
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009 Model 0 Creating, fitting and evaluating a baseline model for SkimLit_en.srt - 11.5 KB
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028 Model 5 Building the foundations of a tribrid embedding model_en.srt - 11.43 KB
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023 Crafting multi-input fast loading tf.data datasets for Model 4_en.srt - 10.89 KB
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007 Performing visual data analysis on our preprocessed text_en.srt - 10.88 KB
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031 Creating multi-level data input pipelines for Model 5 with the tf.data API_en.srt - 10.74 KB
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018 Creating a character-level embedding layer with tf.keras.layers.Embedding_en.srt - 10.42 KB
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025 Model 5 Adding positional embeddings via feature engineering (overview)_en.srt - 10.18 KB
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027 Encoding the total lines feature to be used with Model 5_en.srt - 10.15 KB
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034 Saving, loading & testing our best performing model_en.srt - 10.02 KB
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020 Discussing how we're going to build Model 4 (character + token embeddings)_en.srt - 8.74 KB
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036 Milestone Project 2 (SkimLit) challenge, exercises and extra-curriculum.html - 1.53 KB
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05 - Computer Vision and Convolutional Neural Networks in TensorFlow
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015 Breaking our CNN model down part 5 Looking inside a Conv2D layer.mp4 - 186.73 MB
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020 Breaking our CNN model down part 10 Visualizing our augmented data.mp4 - 156.24 MB
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007 Building an end to end CNN Model.mp4 - 151.01 MB
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027 Multi-class CNN's part 1 Becoming one with the data.mp4 - 137.16 MB
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032 Multi-class CNN's part 6 Trying to fix overfitting by removing layers.mp4 - 128.8 MB
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008 Using a GPU to run our CNN model 5x faster.mp4 - 115.33 MB
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012 Breaking our CNN model down part 2 Preparing to load our data.mp4 - 107.28 MB
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010 Improving our non-CNN model by adding more layers.mp4 - 105.94 MB
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025 Writing a helper function to load and preprocessing custom images.mp4 - 104.39 MB
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009 Trying a non-CNN model on our image data.mp4 - 100.22 MB
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026 Making a prediction on a custom image with our trained CNN.mp4 - 98.64 MB
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011 Breaking our CNN model down part 1 Becoming one with the data.mp4 - 89.98 MB
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029 Multi-class CNN's part 3 Building a multi-class CNN model.mp4 - 89.49 MB
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005 Becoming One With Data Part 2.mp4 - 88.06 MB
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017 Breaking our CNN model down part 7 Evaluating our CNN's training curves.mp4 - 87.38 MB
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014 Breaking our CNN model down part 4 Building a baseline CNN model.mp4 - 84.35 MB
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002 Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.mp4 - 74.83 MB
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003 Downloading an image dataset for our first Food Vision model.mp4 - 71.16 MB
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036 Saving and loading our trained CNN model.mp4 - 69.01 MB
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001 Introduction to Computer Vision with TensorFlow.mp4 - 62.93 MB
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030 Multi-class CNN's part 4 Fitting a multi-class CNN model to the data.mp4 - 58.89 MB
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028 Multi-class CNN's part 2 Preparing our data (turning it into tensors).mp4 - 45.71 MB
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004 Becoming One With Data.mp4 - 44.33 MB
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024 Downloading a custom image to make predictions on.mp4 - 43.42 MB
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006 Becoming One With Data Part 3.mp4 - 32.9 MB
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034 Multi-class CNN's part 8 Things you could do to improve your CNN model.mp4 - 26.73 MB
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031 Multi-class CNN's part 5 Evaluating our multi-class CNN model.mp4 - 26.14 MB
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018 Breaking our CNN model down part 8 Reducing overfitting with Max Pooling.encrypted.m4a.part.frag.urls - 141.16 KB
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018 Breaking our CNN model down part 8 Reducing overfitting with Max Pooling.encrypted.mp4.part.frag.urls - 141.16 KB
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033 Multi-class CNN's part 7 Trying to fix overfitting with data augmentation.encrypted.m4a.part.frag.urls - 121.81 KB
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033 Multi-class CNN's part 7 Trying to fix overfitting with data augmentation.encrypted.mp4.part.frag.urls - 121.81 KB
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022 Breaking our CNN model down part 12 Discovering the power of shuffling data.encrypted.m4a.part.frag.urls - 104.58 KB
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022 Breaking our CNN model down part 12 Discovering the power of shuffling data.encrypted.mp4.part.frag.urls - 104.58 KB
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013 Breaking our CNN model down part 3 Loading our data with ImageDataGenerator.encrypted.m4a.part.frag.urls - 103.55 KB
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013 Breaking our CNN model down part 3 Loading our data with ImageDataGenerator.encrypted.mp4.part.frag.urls - 103.55 KB
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035 Multi-class CNN's part 9 Making predictions with our model on custom images.encrypted.m4a.part.frag.urls - 97.39 KB
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035 Multi-class CNN's part 9 Making predictions with our model on custom images.encrypted.mp4.part.frag.urls - 97.39 KB
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021 Breaking our CNN model down part 11 Training a CNN model on augmented data.encrypted.m4a.part.frag.urls - 92.18 KB
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021 Breaking our CNN model down part 11 Training a CNN model on augmented data.encrypted.mp4.part.frag.urls - 92.18 KB
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016 Breaking our CNN model down part 6 Compiling and fitting our baseline CNN.encrypted.mp4.part.frag.urls - 75.72 KB
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016 Breaking our CNN model down part 6 Compiling and fitting our baseline CNN.encrypted.m4a.part.frag.urls - 75.64 KB
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019 Breaking our CNN model down part 9 Reducing overfitting with data augmentation.encrypted.m4a.part.frag.urls - 71.96 KB
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019 Breaking our CNN model down part 9 Reducing overfitting with data augmentation.encrypted.mp4.part.frag.urls - 71.96 KB
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023 Breaking our CNN model down part 13 Exploring options to improve our model.encrypted.m4a.part.frag.urls - 56.33 KB
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023 Breaking our CNN model down part 13 Exploring options to improve our model.encrypted.mp4.part.frag.urls - 56.33 KB
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007 Building an end to end CNN Model_en.srt - 26 KB
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015 Breaking our CNN model down part 5 Looking inside a Conv2D layer_en.srt - 22.79 KB
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027 Multi-class CNN's part 1 Becoming one with the data_en.srt - 22.69 KB
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020 Breaking our CNN model down part 10 Visualizing our augmented data_en.srt - 21.55 KB
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018 Breaking our CNN model down part 8 Reducing overfitting with Max Pooling_en.srt - 19.25 KB
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017 Breaking our CNN model down part 7 Evaluating our CNN's training curves_en.srt - 17.09 KB
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012 Breaking our CNN model down part 2 Preparing to load our data_en.srt - 16.51 KB
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032 Multi-class CNN's part 6 Trying to fix overfitting by removing layers_en.srt - 16.43 KB
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033 Multi-class CNN's part 7 Trying to fix overfitting with data augmentation_en.srt - 16.32 KB
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005 Becoming One With Data Part 2_en.srt - 16.06 KB
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026 Making a prediction on a custom image with our trained CNN_en.srt - 15.46 KB
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001 Introduction to Computer Vision with TensorFlow_en.srt - 15 KB
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022 Breaking our CNN model down part 12 Discovering the power of shuffling data_en.srt - 14.3 KB
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010 Improving our non-CNN model by adding more layers_en.srt - 13.98 KB
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025 Writing a helper function to load and preprocessing custom images_en.srt - 13.73 KB
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021 Breaking our CNN model down part 11 Training a CNN model on augmented data_en.srt - 13.58 KB
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013 Breaking our CNN model down part 3 Loading our data with ImageDataGenerator_en.srt - 13.46 KB
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008 Using a GPU to run our CNN model 5x faster_en.srt - 13.05 KB
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011 Breaking our CNN model down part 1 Becoming one with the data_en.srt - 13 KB
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002 Introduction to Convolutional Neural Networks (CNNs) with TensorFlow_en.srt - 12.11 KB
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035 Multi-class CNN's part 9 Making predictions with our model on custom images_en.srt - 11.9 KB
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009 Trying a non-CNN model on our image data_en.srt - 11.63 KB
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014 Breaking our CNN model down part 4 Building a baseline CNN model_en.srt - 11.22 KB
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029 Multi-class CNN's part 3 Building a multi-class CNN model_en.srt - 10.65 KB
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003 Downloading an image dataset for our first Food Vision model_en.srt - 10.32 KB
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028 Multi-class CNN's part 2 Preparing our data (turning it into tensors)_en.srt - 9.96 KB
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016 Breaking our CNN model down part 6 Compiling and fitting our baseline CNN_en.srt - 9.86 KB
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019 Breaking our CNN model down part 9 Reducing overfitting with data augmentation_en.srt - 9.39 KB
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036 Saving and loading our trained CNN model_en.srt - 9.07 KB
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030 Multi-class CNN's part 4 Fitting a multi-class CNN model to the data_en.srt - 8.96 KB
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023 Breaking our CNN model down part 13 Exploring options to improve our model_en.srt - 7.53 KB
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024 Downloading a custom image to make predictions on_en.srt - 6.94 KB
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031 Multi-class CNN's part 5 Evaluating our multi-class CNN model_en.srt - 6.79 KB
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004 Becoming One With Data_en.srt - 6.72 KB
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006 Becoming One With Data Part 3_en.srt - 6.54 KB
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034 Multi-class CNN's part 8 Things you could do to improve your CNN model_en.srt - 6.18 KB
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037 TensorFlow computer vision and CNNs challenge, exercises & extra-curriculum.html - 2.48 KB
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013 Breaking our CNN model down part 3 Loading our data with ImageDataGenerator.encrypted.m4a.part - 0 B
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013 Breaking our CNN model down part 3 Loading our data with ImageDataGenerator.encrypted.mp4.part - 0 B
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016 Breaking our CNN model down part 6 Compiling and fitting our baseline CNN.encrypted.m4a.part - 0 B
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016 Breaking our CNN model down part 6 Compiling and fitting our baseline CNN.encrypted.mp4.part - 0 B
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018 Breaking our CNN model down part 8 Reducing overfitting with Max Pooling.encrypted.m4a.part - 0 B
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018 Breaking our CNN model down part 8 Reducing overfitting with Max Pooling.encrypted.mp4.part - 0 B
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019 Breaking our CNN model down part 9 Reducing overfitting with data augmentation.encrypted.m4a.part - 0 B
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019 Breaking our CNN model down part 9 Reducing overfitting with data augmentation.encrypted.mp4.part - 0 B
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021 Breaking our CNN model down part 11 Training a CNN model on augmented data.encrypted.m4a.part - 0 B
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021 Breaking our CNN model down part 11 Training a CNN model on augmented data.encrypted.mp4.part - 0 B
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022 Breaking our CNN model down part 12 Discovering the power of shuffling data.encrypted.m4a.part - 0 B
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022 Breaking our CNN model down part 12 Discovering the power of shuffling data.encrypted.mp4.part - 0 B
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023 Breaking our CNN model down part 13 Exploring options to improve our model.encrypted.m4a.part - 0 B
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023 Breaking our CNN model down part 13 Exploring options to improve our model.encrypted.mp4.part - 0 B
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033 Multi-class CNN's part 7 Trying to fix overfitting with data augmentation.encrypted.m4a.part - 0 B
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033 Multi-class CNN's part 7 Trying to fix overfitting with data augmentation.encrypted.mp4.part - 0 B
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035 Multi-class CNN's part 9 Making predictions with our model on custom images.encrypted.m4a.part - 0 B
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035 Multi-class CNN's part 9 Making predictions with our model on custom images.encrypted.mp4.part - 0 B
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12 - Time Series fundamentals in TensorFlow + Milestone Project 3 BitPredict
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050 Model 8 Building, compiling and fitting an ensemble of models.mp4 - 175.66 MB
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045 Model 7 Getting ready for residual connections.mp4 - 147.61 MB
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008 Downloading and inspecting our Bitcoin historical dataset.mp4 - 147.33 MB
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027 Creating a function to make predictions with our trained models.mp4 - 119.08 MB
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054 Plotting the prediction intervals of our ensemble model predictions.mp4 - 117.45 MB
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059 Model 9 Creating a function to make forecasts into the future.mp4 - 117.03 MB
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016 Model 0 Making and visualizing a naive forecast model.mp4 - 112.28 MB
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033 Preparing data for building a Conv1D model.mp4 - 109.51 MB
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060 Model 9 Plotting our model's future forecasts.mp4 - 102.93 MB
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018 Implementing MASE with TensorFlow.mp4 - 101.31 MB
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011 Reading in our Bitcoin data with Python's CSV module.mp4 - 100.97 MB
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038 Preparing our multivariate time series for a model.mp4 - 97.98 MB
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032 Comparing our modelling experiments so far and discussing autocorrelation.mp4 - 92.91 MB
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031 Model 3 Visualizing the results.mp4 - 85.81 MB
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053 Getting the upper and lower bounds of our prediction intervals.mp4 - 81.54 MB
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061 Model 10 Introducing the turkey problem and making data for it.mp4 - 72.13 MB
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022 Discussing the use of windows and horizons in time series data.mp4 - 68.08 MB
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005 What can be forecast.mp4 - 65.08 MB
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044 Model 7 Setting up hyperparameters for the N-BEATS algorithm.mp4 - 64.47 MB
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012 Creating train and test splits for time series (the wrong way).mp4 - 61.02 MB
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013 Creating train and test splits for time series (the right way).mp4 - 46.43 MB
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014 Creating a plotting function to visualize our time series data.mp4 - 45.59 MB
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049 Model 8 Ensemble model overview.mp4 - 36.63 MB
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010 Visualizing our Bitcoin historical data with pandas.mp4 - 34.93 MB
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007 Time series forecasting inputs and outputs.mp4 - 28.46 MB
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006 What we're going to cover (broadly).mp4 - 25.32 MB
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004 Example forecasting problems in daily life.mp4 - 22.61 MB
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023 Writing a preprocessing function to turn time series data into windows & labels.encrypted.m4a.part.frag.urls - 244.19 KB
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023 Writing a preprocessing function to turn time series data into windows & labels.encrypted.mp4.part.frag.urls - 244.19 KB
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047 Model 7 Putting together the pieces of the puzzle of the N-BEATS model.encrypted.m4a.part.frag.urls - 229.84 KB
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047 Model 7 Putting together the pieces of the puzzle of the N-BEATS model.encrypted.mp4.part.frag.urls - 229.84 KB
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041 Model 7 Replicating the N-BEATS basic block with TensorFlow layer subclassing.encrypted.m4a.part.frag.urls - 193.29 KB
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041 Model 7 Replicating the N-BEATS basic block with TensorFlow layer subclassing.encrypted.mp4.part.frag.urls - 193.29 KB
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028 Model 2 Building, fitting and evaluating a deep model with a larger window size.encrypted.m4a.part.frag.urls - 184.38 KB
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028 Model 2 Building, fitting and evaluating a deep model with a larger window size.encrypted.mp4.part.frag.urls - 184.38 KB
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026 Model 1 Building, compiling and fitting a deep learning model on Bitcoin data.encrypted.m4a.part.frag.urls - 175.8 KB
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026 Model 1 Building, compiling and fitting a deep learning model on Bitcoin data.encrypted.mp4.part.frag.urls - 175.8 KB
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035 Model 5 Building, fitting and evaluating a LSTM (RNN) model on our Bitcoin data.encrypted.m4a.part.frag.urls - 166.85 KB
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035 Model 5 Building, fitting and evaluating a LSTM (RNN) model on our Bitcoin data.encrypted.mp4.part.frag.urls - 166.85 KB
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051 Model 8 Making and evaluating predictions with our ensemble model.encrypted.m4a.part.frag.urls - 165.65 KB
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051 Model 8 Making and evaluating predictions with our ensemble model.encrypted.mp4.part.frag.urls - 165.65 KB
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042 Model 7 Testing our N-BEATS block implementation with dummy data inputs.encrypted.m4a.part.frag.urls - 155.35 KB
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042 Model 7 Testing our N-BEATS block implementation with dummy data inputs.encrypted.mp4.part.frag.urls - 155.35 KB
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034 Model 4 Building, fitting and evaluating a Conv1D model on our Bitcoin data.encrypted.mp4.part.frag.urls - 153.89 KB
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034 Model 4 Building, fitting and evaluating a Conv1D model on our Bitcoin data.encrypted.m4a.part.frag.urls - 153.74 KB
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043 Model 7 Creating a performant data pipeline for the N-BEATS model with tf.data.encrypted.m4a.part.frag.urls - 147.12 KB
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043 Model 7 Creating a performant data pipeline for the N-BEATS model with tf.data.encrypted.mp4.part.frag.urls - 147.12 KB
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036 Investigating how to turn our univariate time series into multivariate.encrypted.m4a.part.frag.urls - 142.93 KB
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036 Investigating how to turn our univariate time series into multivariate.encrypted.mp4.part.frag.urls - 142.93 KB
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062 Model 10 Building a model to predict on turkey data (why forecasting is BS).encrypted.m4a.part.frag.urls - 141.56 KB
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062 Model 10 Building a model to predict on turkey data (why forecasting is BS).encrypted.mp4.part.frag.urls - 141.56 KB
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055 (Optional) Discussing the types of uncertainty in machine learning.encrypted.m4a.part.frag.urls - 140.35 KB
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055 (Optional) Discussing the types of uncertainty in machine learning.encrypted.mp4.part.frag.urls - 140.35 KB
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029 Model 3 Building, fitting and evaluating a model with a larger horizon size.encrypted.m4a.part.frag.urls - 137.45 KB
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029 Model 3 Building, fitting and evaluating a model with a larger horizon size.encrypted.mp4.part.frag.urls - 137.45 KB
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021 Formatting data Part 2 Creating a function to label our windowed time series.encrypted.m4a.part.frag.urls - 135.53 KB
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021 Formatting data Part 2 Creating a function to label our windowed time series.encrypted.mp4.part.frag.urls - 135.53 KB
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063 Comparing the results of all of our models and discussing where to go next.encrypted.m4a.part.frag.urls - 134.24 KB
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063 Comparing the results of all of our models and discussing where to go next.encrypted.mp4.part.frag.urls - 134.24 KB
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052 Discussing the importance of prediction intervals in forecasting.encrypted.m4a.part.frag.urls - 132.96 KB
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052 Discussing the importance of prediction intervals in forecasting.encrypted.mp4.part.frag.urls - 132.96 KB
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037 Creating and plotting a multivariate time series with BTC price and block reward.encrypted.mp4.part.frag.urls - 127.78 KB
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037 Creating and plotting a multivariate time series with BTC price and block reward.encrypted.m4a.part.frag.urls - 127.66 KB
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017 Discussing some of the most common time series evaluation metrics.encrypted.m4a.part.frag.urls - 114.77 KB
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017 Discussing some of the most common time series evaluation metrics.encrypted.mp4.part.frag.urls - 114.77 KB
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019 Creating a function to evaluate our model's forecasts with various metrics.encrypted.m4a.part.frag.urls - 105.5 KB
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019 Creating a function to evaluate our model's forecasts with various metrics.encrypted.mp4.part.frag.urls - 105.5 KB
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046 Model 7 Outlining the steps we're going to take to build the N-BEATS model.encrypted.m4a.part.frag.urls - 104.48 KB
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046 Model 7 Outlining the steps we're going to take to build the N-BEATS model.encrypted.mp4.part.frag.urls - 104.48 KB
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024 Turning our windowed time series data into training and test sets.encrypted.m4a.part.frag.urls - 103.58 KB
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024 Turning our windowed time series data into training and test sets.encrypted.mp4.part.frag.urls - 103.58 KB
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040 Model 7 Discussing what we're going to be doing with the N-BEATS algorithm.encrypted.m4a.part.frag.urls - 100.38 KB
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040 Model 7 Discussing what we're going to be doing with the N-BEATS algorithm.encrypted.mp4.part.frag.urls - 100.38 KB
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039 Model 6 Building, fitting and evaluating a multivariate time series model.encrypted.m4a.part.frag.urls - 98.23 KB
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039 Model 6 Building, fitting and evaluating a multivariate time series model.encrypted.mp4.part.frag.urls - 98.23 KB
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015 Discussing the various modelling experiments were going to be running.encrypted.m4a.part.frag.urls - 94.8 KB
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015 Discussing the various modelling experiments were going to be running.encrypted.mp4.part.frag.urls - 94.8 KB
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030 Adjusting the evaluation function to work for predictions with larger horizons.encrypted.m4a.part.frag.urls - 89.45 KB
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030 Adjusting the evaluation function to work for predictions with larger horizons.encrypted.mp4.part.frag.urls - 89.45 KB
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058 Model 9 Discussing what's required for our model to make future predictions.encrypted.m4a.part.frag.urls - 89.19 KB
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058 Model 9 Discussing what's required for our model to make future predictions.encrypted.mp4.part.frag.urls - 89.19 KB
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056 Model 9 Preparing data to create a model capable of predicting into the future.encrypted.m4a.part.frag.urls - 88.42 KB
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056 Model 9 Preparing data to create a model capable of predicting into the future.encrypted.mp4.part.frag.urls - 88.42 KB
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003 What is a time series problem and example forecasting problems at Uber.encrypted.m4a.part.frag.urls - 80.6 KB
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003 What is a time series problem and example forecasting problems at Uber.encrypted.mp4.part.frag.urls - 80.6 KB
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009 Different kinds of time series patterns & different amounts of feature variables.encrypted.m4a.part.frag.urls - 80.34 KB
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009 Different kinds of time series patterns & different amounts of feature variables.encrypted.mp4.part.frag.urls - 80.34 KB
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025 Creating a modelling checkpoint callback to save our best performing model.encrypted.m4a.part.frag.urls - 77.84 KB
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025 Creating a modelling checkpoint callback to save our best performing model.encrypted.mp4.part.frag.urls - 77.84 KB
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048 Model 7 Plotting the N-BEATS algorithm we've created and admiring its beauty.encrypted.m4a.part.frag.urls - 70.8 KB
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048 Model 7 Plotting the N-BEATS algorithm we've created and admiring its beauty.encrypted.mp4.part.frag.urls - 70.8 KB
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020 Discussing other non-TensorFlow kinds of time series forecasting models.encrypted.m4a.part.frag.urls - 54.12 KB
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020 Discussing other non-TensorFlow kinds of time series forecasting models.encrypted.mp4.part.frag.urls - 54.12 KB
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057 Model 9 Building, compiling and fitting a future predictions model.encrypted.m4a.part.frag.urls - 52.85 KB
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057 Model 9 Building, compiling and fitting a future predictions model.encrypted.mp4.part.frag.urls - 52.85 KB
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002 Introduction to Milestone Project 3 (BitPredict) & where you can get help.encrypted.m4a.part.frag.urls - 40.92 KB
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002 Introduction to Milestone Project 3 (BitPredict) & where you can get help.encrypted.mp4.part.frag.urls - 40.92 KB
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023 Writing a preprocessing function to turn time series data into windows & labels_en.srt - 31.36 KB
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047 Model 7 Putting together the pieces of the puzzle of the N-BEATS model_en.srt - 30.09 KB
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050 Model 8 Building, compiling and fitting an ensemble of models_en.srt - 28.8 KB
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028 Model 2 Building, fitting and evaluating a deep model with a larger window size_en.srt - 26.52 KB
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041 Model 7 Replicating the N-BEATS basic block with TensorFlow layer subclassing_en.srt - 26.08 KB
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026 Model 1 Building, compiling and fitting a deep learning model on Bitcoin data_en.srt - 25.71 KB
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051 Model 8 Making and evaluating predictions with our ensemble model_en.srt - 22.44 KB
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042 Model 7 Testing our N-BEATS block implementation with dummy data inputs_en.srt - 21.68 KB
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035 Model 5 Building, fitting and evaluating a LSTM (RNN) model on our Bitcoin data_en.srt - 21.58 KB
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008 Downloading and inspecting our Bitcoin historical dataset_en.srt - 21.38 KB
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034 Model 4 Building, fitting and evaluating a Conv1D model on our Bitcoin data_en.srt - 20.55 KB
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027 Creating a function to make predictions with our trained models_en.srt - 19.7 KB
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063 Comparing the results of all of our models and discussing where to go next_en.srt - 19.58 KB
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061 Model 10 Introducing the turkey problem and making data for it_en.srt - 19.17 KB
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043 Model 7 Creating a performant data pipeline for the N-BEATS model with tf.data_en.srt - 19.14 KB
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062 Model 10 Building a model to predict on turkey data (why forecasting is BS)_en.srt - 18.89 KB
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029 Model 3 Building, fitting and evaluating a model with a larger horizon size_en.srt - 18.83 KB
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033 Preparing data for building a Conv1D model_en.srt - 18.71 KB
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055 (Optional) Discussing the types of uncertainty in machine learning_en.srt - 18.48 KB
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021 Formatting data Part 2 Creating a function to label our windowed time series_en.srt - 18.33 KB
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036 Investigating how to turn our univariate time series into multivariate_en.srt - 17.89 KB
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038 Preparing our multivariate time series for a model_en.srt - 17.52 KB
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054 Plotting the prediction intervals of our ensemble model predictions_en.srt - 17.46 KB
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016 Model 0 Making and visualizing a naive forecast model_en.srt - 17.38 KB
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060 Model 9 Plotting our model's future forecasts_en.srt - 17.28 KB
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045 Model 7 Getting ready for residual connections_en.srt - 17.16 KB
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052 Discussing the importance of prediction intervals in forecasting_en.srt - 16.84 KB
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017 Discussing some of the most common time series evaluation metrics_en.srt - 16.31 KB
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011 Reading in our Bitcoin data with Python's CSV module_en.srt - 15.91 KB
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059 Model 9 Creating a function to make forecasts into the future_en.srt - 15.85 KB
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007 Time series forecasting inputs and outputs_en.srt - 15.49 KB
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037 Creating and plotting a multivariate time series with BTC price and block reward_en.srt - 14.48 KB
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032 Comparing our modelling experiments so far and discussing autocorrelation_en.srt - 14.17 KB
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024 Turning our windowed time series data into training and test sets_en.srt - 13.97 KB
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015 Discussing the various modelling experiments were going to be running_en.srt - 13.79 KB
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019 Creating a function to evaluate our model's forecasts with various metrics_en.srt - 13.77 KB
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046 Model 7 Outlining the steps we're going to take to build the N-BEATS model_en.srt - 13.76 KB
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040 Model 7 Discussing what we're going to be doing with the N-BEATS algorithm_en.srt - 13.29 KB
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003 What is a time series problem and example forecasting problems at Uber_en.srt - 13.15 KB
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005 What can be forecast_en.srt - 13.13 KB
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039 Model 6 Building, fitting and evaluating a multivariate time series model_en.srt - 13.04 KB
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044 Model 7 Setting up hyperparameters for the N-BEATS algorithm_en.srt - 13 KB
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031 Model 3 Visualizing the results_en.srt - 12.9 KB
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022 Discussing the use of windows and horizons in time series data_en.srt - 12.74 KB
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009 Different kinds of time series patterns & different amounts of feature variables_en.srt - 11.87 KB
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012 Creating train and test splits for time series (the wrong way)_en.srt - 11.6 KB
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058 Model 9 Discussing what's required for our model to make future predictions_en.srt - 11.48 KB
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018 Implementing MASE with TensorFlow_en.vtt - 11.1 KB
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030 Adjusting the evaluation function to work for predictions with larger horizons_en.srt - 11.06 KB
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056 Model 9 Preparing data to create a model capable of predicting into the future_en.srt - 10.66 KB
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025 Creating a modelling checkpoint callback to save our best performing model_en.srt - 10.65 KB
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048 Model 7 Plotting the N-BEATS algorithm we've created and admiring its beauty_en.srt - 10.49 KB
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053 Getting the upper and lower bounds of our prediction intervals_en.srt - 10.41 KB
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014 Creating a plotting function to visualize our time series data_en.srt - 10.3 KB
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013 Creating train and test splits for time series (the right way)_en.srt - 10.28 KB
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004 Example forecasting problems in daily life_en.srt - 7.97 KB
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010 Visualizing our Bitcoin historical data with pandas_en.srt - 7.35 KB
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057 Model 9 Building, compiling and fitting a future predictions model_en.srt - 7.1 KB
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049 Model 8 Ensemble model overview_en.srt - 6.91 KB
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020 Discussing other non-TensorFlow kinds of time series forecasting models_en.vtt - 6.75 KB
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002 Introduction to Milestone Project 3 (BitPredict) & where you can get help_en.srt - 6.5 KB
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006 What we're going to cover (broadly)_en.srt - 4.45 KB
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018 Implementing MASE with TensorFlow_en.srt - 3.82 KB
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020 Discussing other non-TensorFlow kinds of time series forecasting models_en.srt - 2.7 KB
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064 TensorFlow Time Series Fundamentals Challenge and Extra Resources.html - 1.86 KB
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001 Welcome to time series fundamentals with TensorFlow + Milestone Project 3!.html - 1.32 KB
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002 Introduction to Milestone Project 3 (BitPredict) & where you can get help.encrypted.m4a.part - 0 B
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002 Introduction to Milestone Project 3 (BitPredict) & where you can get help.encrypted.mp4.part - 0 B
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003 What is a time series problem and example forecasting problems at Uber.encrypted.m4a.part - 0 B
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003 What is a time series problem and example forecasting problems at Uber.encrypted.mp4.part - 0 B
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009 Different kinds of time series patterns & different amounts of feature variables.encrypted.m4a.part - 0 B
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009 Different kinds of time series patterns & different amounts of feature variables.encrypted.mp4.part - 0 B
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015 Discussing the various modelling experiments were going to be running.encrypted.m4a.part - 0 B
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015 Discussing the various modelling experiments were going to be running.encrypted.mp4.part - 0 B
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017 Discussing some of the most common time series evaluation metrics.encrypted.m4a.part - 0 B
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017 Discussing some of the most common time series evaluation metrics.encrypted.mp4.part - 0 B
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019 Creating a function to evaluate our model's forecasts with various metrics.encrypted.m4a.part - 0 B
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019 Creating a function to evaluate our model's forecasts with various metrics.encrypted.mp4.part - 0 B
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020 Discussing other non-TensorFlow kinds of time series forecasting models.encrypted.m4a.part - 0 B
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020 Discussing other non-TensorFlow kinds of time series forecasting models.encrypted.mp4.part - 0 B
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021 Formatting data Part 2 Creating a function to label our windowed time series.encrypted.m4a.part - 0 B
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021 Formatting data Part 2 Creating a function to label our windowed time series.encrypted.mp4.part - 0 B
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023 Writing a preprocessing function to turn time series data into windows & labels.encrypted.m4a.part - 0 B
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023 Writing a preprocessing function to turn time series data into windows & labels.encrypted.mp4.part - 0 B
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024 Turning our windowed time series data into training and test sets.encrypted.m4a.part - 0 B
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024 Turning our windowed time series data into training and test sets.encrypted.mp4.part - 0 B
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025 Creating a modelling checkpoint callback to save our best performing model.encrypted.m4a.part - 0 B
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025 Creating a modelling checkpoint callback to save our best performing model.encrypted.mp4.part - 0 B
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026 Model 1 Building, compiling and fitting a deep learning model on Bitcoin data.encrypted.m4a.part - 0 B
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026 Model 1 Building, compiling and fitting a deep learning model on Bitcoin data.encrypted.mp4.part - 0 B
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028 Model 2 Building, fitting and evaluating a deep model with a larger window size.encrypted.m4a.part - 0 B
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028 Model 2 Building, fitting and evaluating a deep model with a larger window size.encrypted.mp4.part - 0 B
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029 Model 3 Building, fitting and evaluating a model with a larger horizon size.encrypted.m4a.part - 0 B
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029 Model 3 Building, fitting and evaluating a model with a larger horizon size.encrypted.mp4.part - 0 B
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030 Adjusting the evaluation function to work for predictions with larger horizons.encrypted.m4a.part - 0 B
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030 Adjusting the evaluation function to work for predictions with larger horizons.encrypted.mp4.part - 0 B
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034 Model 4 Building, fitting and evaluating a Conv1D model on our Bitcoin data.encrypted.m4a.part - 0 B
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034 Model 4 Building, fitting and evaluating a Conv1D model on our Bitcoin data.encrypted.mp4.part - 0 B
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035 Model 5 Building, fitting and evaluating a LSTM (RNN) model on our Bitcoin data.encrypted.m4a.part - 0 B
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035 Model 5 Building, fitting and evaluating a LSTM (RNN) model on our Bitcoin data.encrypted.mp4.part - 0 B
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036 Investigating how to turn our univariate time series into multivariate.encrypted.m4a.part - 0 B
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036 Investigating how to turn our univariate time series into multivariate.encrypted.mp4.part - 0 B
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037 Creating and plotting a multivariate time series with BTC price and block reward.encrypted.m4a.part - 0 B
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037 Creating and plotting a multivariate time series with BTC price and block reward.encrypted.mp4.part - 0 B
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039 Model 6 Building, fitting and evaluating a multivariate time series model.encrypted.m4a.part - 0 B
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039 Model 6 Building, fitting and evaluating a multivariate time series model.encrypted.mp4.part - 0 B
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040 Model 7 Discussing what we're going to be doing with the N-BEATS algorithm.encrypted.m4a.part - 0 B
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040 Model 7 Discussing what we're going to be doing with the N-BEATS algorithm.encrypted.mp4.part - 0 B
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041 Model 7 Replicating the N-BEATS basic block with TensorFlow layer subclassing.encrypted.m4a.part - 0 B
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041 Model 7 Replicating the N-BEATS basic block with TensorFlow layer subclassing.encrypted.mp4.part - 0 B
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042 Model 7 Testing our N-BEATS block implementation with dummy data inputs.encrypted.m4a.part - 0 B
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042 Model 7 Testing our N-BEATS block implementation with dummy data inputs.encrypted.mp4.part - 0 B
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043 Model 7 Creating a performant data pipeline for the N-BEATS model with tf.data.encrypted.m4a.part - 0 B
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043 Model 7 Creating a performant data pipeline for the N-BEATS model with tf.data.encrypted.mp4.part - 0 B
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046 Model 7 Outlining the steps we're going to take to build the N-BEATS model.encrypted.m4a.part - 0 B
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046 Model 7 Outlining the steps we're going to take to build the N-BEATS model.encrypted.mp4.part - 0 B
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047 Model 7 Putting together the pieces of the puzzle of the N-BEATS model.encrypted.m4a.part - 0 B
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047 Model 7 Putting together the pieces of the puzzle of the N-BEATS model.encrypted.mp4.part - 0 B
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048 Model 7 Plotting the N-BEATS algorithm we've created and admiring its beauty.encrypted.m4a.part - 0 B
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048 Model 7 Plotting the N-BEATS algorithm we've created and admiring its beauty.encrypted.mp4.part - 0 B
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051 Model 8 Making and evaluating predictions with our ensemble model.encrypted.m4a.part - 0 B
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051 Model 8 Making and evaluating predictions with our ensemble model.encrypted.mp4.part - 0 B
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052 Discussing the importance of prediction intervals in forecasting.encrypted.m4a.part - 0 B
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052 Discussing the importance of prediction intervals in forecasting.encrypted.mp4.part - 0 B
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055 (Optional) Discussing the types of uncertainty in machine learning.encrypted.m4a.part - 0 B
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055 (Optional) Discussing the types of uncertainty in machine learning.encrypted.mp4.part - 0 B
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056 Model 9 Preparing data to create a model capable of predicting into the future.encrypted.m4a.part - 0 B
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056 Model 9 Preparing data to create a model capable of predicting into the future.encrypted.mp4.part - 0 B
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057 Model 9 Building, compiling and fitting a future predictions model.encrypted.m4a.part - 0 B
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057 Model 9 Building, compiling and fitting a future predictions model.encrypted.mp4.part - 0 B
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058 Model 9 Discussing what's required for our model to make future predictions.encrypted.m4a.part - 0 B
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058 Model 9 Discussing what's required for our model to make future predictions.encrypted.mp4.part - 0 B
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062 Model 10 Building a model to predict on turkey data (why forecasting is BS).encrypted.m4a.part - 0 B
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062 Model 10 Building a model to predict on turkey data (why forecasting is BS).encrypted.mp4.part - 0 B
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063 Comparing the results of all of our models and discussing where to go next.encrypted.m4a.part - 0 B
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063 Comparing the results of all of our models and discussing where to go next.encrypted.mp4.part - 0 B
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08 - Transfer Learning with TensorFlow Part 3 Scaling Up
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018 Making predictions on our test images and evaluating them.mp4 - 168.63 MB
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014 Creating a confusion matrix for our model's 101 different classes.mp4 - 157.82 MB
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015 Evaluating every individual class in our dataset.mp4 - 129.66 MB
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002 Getting helper functions ready and downloading data to model.mp4 - 129.4 MB
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011 Making predictions with our trained model on 25,250 test samples.mp4 - 113.22 MB
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020 Writing code to uncover our model's most wrong predictions.mp4 - 108.09 MB
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022 Making predictions on and plotting our own custom images.mp4 - 107.22 MB
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017 Creating a function to load and prepare images for making predictions.mp4 - 106.8 MB
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021 Plotting and visualising the samples our model got most wrong.mp4 - 104.8 MB
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007 Unfreezing some layers in our base model to prepare for fine-tuning.mp4 - 82.84 MB
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010 Downloading a pretrained model to make and evaluate predictions with.mp4 - 78.75 MB
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005 Creating a headless EfficientNetB0 model with data augmentation built in.mp4 - 78.08 MB
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016 Plotting our model's F1-scores for each separate class.mp4 - 77.34 MB
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008 Fine-tuning our feature extraction model and evaluating its performance.mp4 - 64.13 MB
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006 Fitting and evaluating our biggest transfer learning model yet.mp4 - 58.46 MB
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009 Saving and loading our trained model.mp4 - 56.48 MB
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019 Discussing the benefits of finding your model's most wrong predictions.mp4 - 50.78 MB
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013 Confirming our model's predictions are in the same order as the test labels.mp4 - 49.93 MB
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003 Outlining the model we're going to build and building a ModelCheckpoint callback.mp4 - 38.78 MB
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012 Unravelling our test dataset for comparing ground truth labels to predictions.mp4 - 36.9 MB
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001 Introduction to Transfer Learning Part 3 Scaling Up.mp4 - 29.1 MB
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004 Creating a data augmentation layer to use with our model.mp4 - 27.92 MB
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018 Making predictions on our test images and evaluating them_en.srt - 23.48 KB
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015 Evaluating every individual class in our dataset_en.srt - 19.3 KB
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002 Getting helper functions ready and downloading data to model_en.srt - 17.73 KB
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014 Creating a confusion matrix for our model's 101 different classes_en.srt - 17.49 KB
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020 Writing code to uncover our model's most wrong predictions_en.srt - 17.03 KB
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007 Unfreezing some layers in our base model to prepare for fine-tuning_en.srt - 16.61 KB
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011 Making predictions with our trained model on 25,250 test samples_en.srt - 16.24 KB
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017 Creating a function to load and prepare images for making predictions_en.srt - 15.8 KB
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021 Plotting and visualising the samples our model got most wrong_en.srt - 15.46 KB
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022 Making predictions on and plotting our own custom images_en.srt - 14.62 KB
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005 Creating a headless EfficientNetB0 model with data augmentation built in_en.srt - 13.45 KB
-
008 Fine-tuning our feature extraction model and evaluating its performance_en.srt - 11.87 KB
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006 Fitting and evaluating our biggest transfer learning model yet_en.srt - 11.44 KB
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016 Plotting our model's F1-scores for each separate class_en.srt - 10.7 KB
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001 Introduction to Transfer Learning Part 3 Scaling Up_en.srt - 10.12 KB
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019 Discussing the benefits of finding your model's most wrong predictions_en.srt - 9.41 KB
-
009 Saving and loading our trained model_en.srt - 8.98 KB
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010 Downloading a pretrained model to make and evaluate predictions with_en.srt - 8.91 KB
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012 Unravelling our test dataset for comparing ground truth labels to predictions_en.srt - 7.72 KB
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003 Outlining the model we're going to build and building a ModelCheckpoint callback_en.srt - 7.41 KB
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013 Confirming our model's predictions are in the same order as the test labels_en.srt - 6.77 KB
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004 Creating a data augmentation layer to use with our model_en.srt - 6.25 KB
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023 Transfer Learning in TensorFlow Part 3 challenge, exercises and extra-curriculum.html - 2.26 KB
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04 - Neural network classification in TensorFlow
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009 Creating a function to view our model's not so good predictions.mp4 - 159.3 MB
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019 Using callbacks to find a model's ideal learning rate.mp4 - 153.01 MB
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016 Non-linearity part 5 Replicating non-linear activation functions from scratch.mp4 - 145.36 MB
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027 Multi-class classification part 3 Building a multi-class classification model.mp4 - 139.52 MB
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017 Getting great results in less time by tweaking the learning rate.mp4 - 134.35 MB
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007 Building a not very good classification model with TensorFlow.mp4 - 124.5 MB
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034 What patterns is our model learning.mp4 - 124.29 MB
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014 Non-linearity part 3 Upgrading our non-linear model with more layers.mp4 - 123.33 MB
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011 Make our poor classification model work for a regression dataset.mp4 - 122.83 MB
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031 Multi-class classification part 7 Evaluating our model.mp4 - 116.63 MB
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004 Typical architecture of neural network classification models with TensorFlow.mp4 - 112.66 MB
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024 Making our confusion matrix prettier.mp4 - 111.08 MB
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028 Multi-class classification part 4 Improving performance with normalisation.mp4 - 110.92 MB
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005 Creating and viewing classification data to model.mp4 - 104.65 MB
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012 Non-linearity part 1 Straight lines and non-straight lines.mp4 - 94.87 MB
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020 Training and evaluating a model with an ideal learning rate.mp4 - 87.48 MB
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025 Putting things together with multi-class classification part 1 Getting the data.mp4 - 84.67 MB
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008 Trying to improve our not very good classification model.mp4 - 83.26 MB
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001 Introduction to neural network classification in TensorFlow.mp4 - 70.89 MB
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030 Multi-class classification part 6 Finding the ideal learning rate.mp4 - 69.82 MB
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018 Using the TensorFlow History object to plot a model's loss curves.mp4 - 61.69 MB
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013 Non-linearity part 2 Building our first neural network with non-linearity.mp4 - 58.43 MB
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033 Multi-class classification part 9 Visualising random model predictions.mp4 - 56.14 MB
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023 Creating our first confusion matrix (to see where our model is getting confused).mp4 - 54.78 MB
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026 Multi-class classification part 2 Becoming one with the data.mp4 - 46.61 MB
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006 Checking the input and output shapes of our classification data.mp4 - 38.07 MB
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015 Non-linearity part 4 Modelling our non-linear data once and for all.mp4 - 36.23 MB
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032 Multi-class classification part 8 Creating a confusion matrix.mp4 - 33.55 MB
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022 Finding the accuracy of our classification model.mp4 - 32.85 MB
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021 Introducing more classification evaluation methods.mp4 - 28.51 MB
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002 Example classification problems (and their inputs and outputs).mp4 - 19.82 MB
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029 Multi-class classification part 5 Comparing normalised and non-normalised data.mp4 - 18.48 MB
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003 Input and output tensors of classification problems.mp4 - 18.3 MB
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019 Using callbacks to find a model's ideal learning rate_en.srt - 24.88 KB
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027 Multi-class classification part 3 Building a multi-class classification model_en.srt - 21.13 KB
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034 What patterns is our model learning_en.srt - 20.83 KB
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017 Getting great results in less time by tweaking the learning rate_en.srt - 19.38 KB
-
009 Creating a function to view our model's not so good predictions_en.srt - 18.99 KB
-
016 Non-linearity part 5 Replicating non-linear activation functions from scratch_en.srt - 18.29 KB
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024 Making our confusion matrix prettier_en.srt - 18.29 KB
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031 Multi-class classification part 7 Evaluating our model_en.srt - 16.96 KB
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011 Make our poor classification model work for a regression dataset_en.srt - 16.78 KB
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028 Multi-class classification part 4 Improving performance with normalisation_en.srt - 16.22 KB
-
007 Building a not very good classification model with TensorFlow_en.srt - 16.03 KB
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030 Multi-class classification part 6 Finding the ideal learning rate_en.srt - 14.91 KB
-
004 Typical architecture of neural network classification models with TensorFlow_en.srt - 14.61 KB
-
005 Creating and viewing classification data to model_en.srt - 14.39 KB
-
014 Non-linearity part 3 Upgrading our non-linear model with more layers_en.srt - 14.35 KB
-
012 Non-linearity part 1 Straight lines and non-straight lines_en.srt - 13.79 KB
-
025 Putting things together with multi-class classification part 1 Getting the data_en.srt - 13.77 KB
-
033 Multi-class classification part 9 Visualising random model predictions_en.srt - 13.52 KB
-
001 Introduction to neural network classification in TensorFlow_en.srt - 12.76 KB
-
008 Trying to improve our not very good classification model_en.srt - 12.67 KB
-
015 Non-linearity part 4 Modelling our non-linear data once and for all_en.srt - 11.99 KB
-
020 Training and evaluating a model with an ideal learning rate_en.srt - 11.87 KB
-
023 Creating our first confusion matrix (to see where our model is getting confused)_en.srt - 11.54 KB
-
026 Multi-class classification part 2 Becoming one with the data_en.srt - 9.99 KB
-
002 Example classification problems (and their inputs and outputs)_en.srt - 9.89 KB
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021 Introducing more classification evaluation methods_en.srt - 8.87 KB
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003 Input and output tensors of classification problems_en.srt - 8.85 KB
-
018 Using the TensorFlow History object to plot a model's loss curves_en.srt - 8.38 KB
-
013 Non-linearity part 2 Building our first neural network with non-linearity_en.srt - 7.58 KB
-
032 Multi-class classification part 8 Creating a confusion matrix_en.srt - 6.67 KB
-
006 Checking the input and output shapes of our classification data_en.srt - 6.57 KB
-
022 Finding the accuracy of our classification model_en.srt - 5.63 KB
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029 Multi-class classification part 5 Comparing normalised and non-normalised data_en.srt - 5.44 KB
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010 Note Updates for TensorFlow 2.7.0.html - 3.45 KB
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035 TensorFlow classification challenge, exercises & extra-curriculum.html - 2.45 KB
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external-assets-links.txt - 135 B
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06 - Transfer Learning in TensorFlow Part 1 Feature extraction
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010 Comparing Our Model's Results.mp4 - 144.01 MB
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005 Building and compiling a TensorFlow Hub feature extraction model.mp4 - 132.85 MB
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002 Downloading and preparing data for our first transfer learning model.mp4 - 111.54 MB
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009 Different Types of Transfer Learning.mp4 - 110.65 MB
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008 Building and training a pre-trained EfficientNet model on our data.mp4 - 105.94 MB
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006 Blowing our previous models out of the water with transfer learning.mp4 - 98.89 MB
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003 Introducing Callbacks in TensorFlow and making a callback to track our models.mp4 - 92.53 MB
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004 Exploring the TensorFlow Hub website for pretrained models.mp4 - 85.67 MB
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007 Plotting the loss curves of our ResNet feature extraction model.mp4 - 59.98 MB
-
001 What is and why use transfer learning.mp4 - 29.62 MB
-
012 Exercise Imposter Syndrome.mp4 - 26.54 MB
-
010 Comparing Our Model's Results_en.srt - 21.57 KB
-
005 Building and compiling a TensorFlow Hub feature extraction model_en.srt - 18.91 KB
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002 Downloading and preparing data for our first transfer learning model_en.srt - 18.12 KB
-
001 What is and why use transfer learning_en.srt - 15.94 KB
-
009 Different Types of Transfer Learning_en.srt - 15.68 KB
-
004 Exploring the TensorFlow Hub website for pretrained models_en.srt - 14.68 KB
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008 Building and training a pre-trained EfficientNet model on our data_en.srt - 14.27 KB
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003 Introducing Callbacks in TensorFlow and making a callback to track our models_en.srt - 14.26 KB
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006 Blowing our previous models out of the water with transfer learning_en.srt - 13.66 KB
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007 Plotting the loss curves of our ResNet feature extraction model_en.srt - 10.81 KB
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012 Exercise Imposter Syndrome_en.srt - 4.49 KB
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011 TensorFlow Transfer Learning Part 1 challenge, exercises & extra-curriculum.html - 2.42 KB
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external-assets-links.txt - 130 B
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-
02 - Deep Learning and TensorFlow Fundamentals
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010 Creating your first tensors with TensorFlow and tf.constant().mp4 - 130.01 MB
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029 Making sure our tensor operations run really fast on GPUs.mp4 - 110.06 MB
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019 Matrix multiplication with tensors part 2.mp4 - 104.15 MB
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018 Matrix multiplication with tensors part 1.mp4 - 100.89 MB
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014 Creating tensors from NumPy arrays.mp4 - 98.69 MB
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022 Tensor aggregation (finding the min, max, mean & more).mp4 - 88.33 MB
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012 Creating random tensors with TensorFlow.mp4 - 86.04 MB
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015 Getting information from your tensors (tensor attributes).mp4 - 84.13 MB
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024 Finding the positional minimum and maximum of a tensor (argmin and argmax).mp4 - 80.21 MB
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020 Matrix multiplication with tensors part 3.mp4 - 78.49 MB
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016 Indexing and expanding tensors.mp4 - 71.77 MB
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021 Changing the datatype of tensors.mp4 - 70.72 MB
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023 Tensor troubleshooting example (updating tensor datatypes).mp4 - 69.22 MB
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004 What is deep learning already being used for.mp4 - 63.46 MB
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003 What are neural networks.mp4 - 63.38 MB
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026 One-hot encoding tensors.mp4 - 59.12 MB
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002 Why use deep learning.mp4 - 59.09 MB
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011 Creating tensors with TensorFlow and tf.Variable().mp4 - 58.28 MB
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005 What is and why use TensorFlow.mp4 - 57.96 MB
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013 Shuffling the order of tensors.mp4 - 57.86 MB
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027 Trying out more tensor math operations.mp4 - 45.63 MB
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017 Manipulating tensors with basic operations.mp4 - 45.03 MB
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028 Exploring TensorFlow and NumPy's compatibility.mp4 - 42.48 MB
-
001 What is deep learning.mp4 - 35.06 MB
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008 How to approach this course.mp4 - 23.98 MB
-
025 Squeezing a tensor (removing all 1-dimension axes).mp4 - 19.13 MB
-
006 What is a Tensor.mp4 - 18.85 MB
-
007 What we're going to cover throughout the course.mp4 - 12.15 MB
-
010 Creating your first tensors with TensorFlow and tf.constant()_en.srt - 24.75 KB
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019 Matrix multiplication with tensors part 2_en.srt - 17.35 KB
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015 Getting information from your tensors (tensor attributes)_en.srt - 16.96 KB
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016 Indexing and expanding tensors_en.srt - 16.96 KB
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018 Matrix multiplication with tensors part 1_en.srt - 15.22 KB
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014 Creating tensors from NumPy arrays_en.srt - 15.03 KB
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003 What are neural networks_en.srt - 14.7 KB
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029 Making sure our tensor operations run really fast on GPUs_en.srt - 14.46 KB
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002 Why use deep learning_en.srt - 14.2 KB
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004 What is deep learning already being used for_en.srt - 13.48 KB
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020 Matrix multiplication with tensors part 3_en.srt - 13.27 KB
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012 Creating random tensors with TensorFlow_en.srt - 13.03 KB
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022 Tensor aggregation (finding the min, max, mean & more)_en.srt - 12.88 KB
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013 Shuffling the order of tensors_en.srt - 12.63 KB
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024 Finding the positional minimum and maximum of a tensor (argmin and argmax)_en.srt - 12.38 KB
-
005 What is and why use TensorFlow_en.srt - 11.74 KB
-
011 Creating tensors with TensorFlow and tf.Variable()_en.srt - 9.9 KB
-
021 Changing the datatype of tensors_en.srt - 8.65 KB
-
008 How to approach this course_en.srt - 8.24 KB
-
026 One-hot encoding tensors_en.srt - 7.98 KB
-
007 What we're going to cover throughout the course_en.srt - 7.24 KB
-
028 Exploring TensorFlow and NumPy's compatibility_en.srt - 7.11 KB
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017 Manipulating tensors with basic operations_en.srt - 6.95 KB
-
001 What is deep learning_en.srt - 6.94 KB
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023 Tensor troubleshooting example (updating tensor datatypes)_en.srt - 6.64 KB
-
027 Trying out more tensor math operations_en.srt - 6.24 KB
-
006 What is a Tensor_en.srt - 4.99 KB
-
025 Squeezing a tensor (removing all 1-dimension axes)_en.srt - 3.84 KB
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030 TensorFlow Fundamentals challenge, exercises & extra-curriculum.html - 1.94 KB
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031 Monthly Coding Challenges, Free Resources and Guides.html - 1.58 KB
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032 LinkedIn Endorsements.html - 1.37 KB
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009 Need A Refresher.html - 942 B
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external-assets-links.txt - 94 B
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09 - Milestone Project 1 Food Vision Big™
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007 Batching and preparing our datasets (to make them run fast).mp4 - 129.57 MB
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005 Exploring and becoming one with the data (Food101 from TensorFlow Datasets).mp4 - 112.23 MB
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006 Creating a preprocessing function to prepare our data for modelling.mp4 - 111.82 MB
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011 Turning on mixed precision training with TensorFlow.mp4 - 106.53 MB
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012 Creating a feature extraction model capable of using mixed precision training.mp4 - 104.68 MB
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004 Introduction to TensorFlow Datasets (TFDS).mp4 - 97.27 MB
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015 Introducing your Milestone Project 1 challenge build a model to beat DeepFood.mp4 - 89.01 MB
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013 Checking to see if our model is using mixed precision training layer by layer.mp4 - 87.05 MB
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014 Training and evaluating a feature extraction model (Food Vision Big™).mp4 - 86.96 MB
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002 Making sure we have access to the right GPU for mixed precision training.mp4 - 86.17 MB
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009 Creating modelling callbacks for our feature extraction model.mp4 - 58.44 MB
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008 Exploring what happens when we batch and prefetch our data.mp4 - 42.28 MB
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003 Getting helper functions ready.mp4 - 30.72 MB
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001 Introduction to Milestone Project 1 Food Vision Big™.mp4 - 27.37 MB
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005 Exploring and becoming one with the data (Food101 from TensorFlow Datasets)_en.srt - 22.34 KB
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007 Batching and preparing our datasets (to make them run fast)_en.srt - 19.22 KB
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006 Creating a preprocessing function to prepare our data for modelling_en.srt - 18.84 KB
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004 Introduction to TensorFlow Datasets (TFDS)_en.srt - 17.62 KB
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012 Creating a feature extraction model capable of using mixed precision training_en.srt - 17.42 KB
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014 Training and evaluating a feature extraction model (Food Vision Big™)_en.srt - 14.12 KB
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002 Making sure we have access to the right GPU for mixed precision training_en.srt - 14.06 KB
-
011 Turning on mixed precision training with TensorFlow_en.srt - 13.9 KB
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015 Introducing your Milestone Project 1 challenge build a model to beat DeepFood_en.srt - 11.24 KB
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013 Checking to see if our model is using mixed precision training layer by layer_en.srt - 10.27 KB
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009 Creating modelling callbacks for our feature extraction model_en.srt - 9.85 KB
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008 Exploring what happens when we batch and prefetch our data_en.srt - 9.41 KB
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001 Introduction to Milestone Project 1 Food Vision Big™_en.srt - 9.18 KB
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003 Getting helper functions ready_en.srt - 3.94 KB
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016 Milestone Project 1 Food Vision Big™, exercises and extra-curriculum.html - 2.31 KB
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010 Note Mixed Precision producing errors for TensorFlow 2.5+.html - 1.92 KB
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17 - Appendix Pandas for Data Analysis
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009 Selecting and Viewing Data with Pandas Part 2.mp4 - 103.44 MB
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010 Manipulating Data.mp4 - 100.87 MB
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005 Series, Data Frames and CSVs.mp4 - 91.12 MB
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011 Manipulating Data 2.mp4 - 84.34 MB
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012 Manipulating Data 3.mp4 - 76.74 MB
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014 How To Download The Course Assignments.mp4 - 64.53 MB
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007 Describing Data with Pandas.mp4 - 63.45 MB
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008 Selecting and Viewing Data with Pandas.mp4 - 61.7 MB
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004 Pandas Introduction.mp4 - 11.09 MB
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002 Section Overview.mp4 - 5.07 MB
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005 pandas-anatomy-of-a-dataframe.png - 333.24 KB
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011 pandas-anatomy-of-a-dataframe.png - 333.24 KB
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009 Selecting and Viewing Data with Pandas Part 2_en.srt - 18.95 KB
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010 Manipulating Data_en.srt - 18.56 KB
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005 Series, Data Frames and CSVs_en.srt - 18.45 KB
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008 Selecting and Viewing Data with Pandas_en.srt - 15.22 KB
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011 Manipulating Data 2_en.srt - 14.82 KB
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007 Describing Data with Pandas_en.srt - 14.23 KB
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012 Manipulating Data 3_en.srt - 14.01 KB
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014 How To Download The Course Assignments_en.srt - 11.24 KB
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004 Pandas Introduction_en.srt - 6.91 KB
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002 Section Overview_en.srt - 3.69 KB
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013 Assignment Pandas Practice.html - 2.05 KB
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006 Data from URLs.html - 1.08 KB
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external-assets-links.txt - 1.02 KB
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003 Downloading Workbooks and Assignments.html - 967 B
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001 Quick Note Upcoming Videos.html - 706 B
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008 car-sales.csv - 369 B
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010 car-sales-missing-data.csv - 287 B
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18 - Appendix NumPy
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014 Exercise Nut Butter Store Sales.mp4 - 87.15 MB
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017 Turn Images Into NumPy Arrays.mp4 - 71.27 MB
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013 Dot Product vs Element Wise.mp4 - 69.85 MB
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009 Manipulating Arrays.mp4 - 68.43 MB
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008 Viewing Arrays and Matrices.mp4 - 59.34 MB
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010 Manipulating Arrays 2.mp4 - 58.05 MB
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005 NumPy DataTypes and Attributes.mp4 - 56.56 MB
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012 Reshape and Transpose.mp4 - 51.44 MB
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006 Creating NumPy Arrays.mp4 - 45.63 MB
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011 Standard Deviation and Variance.mp4 - 36.81 MB
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007 NumPy Random Seed.mp4 - 36.47 MB
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016 Sorting Arrays.mp4 - 24.27 MB
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015 Comparison Operators.mp4 - 22.02 MB
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003 NumPy Introduction.mp4 - 13.62 MB
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002 Section Overview.mp4 - 12.47 MB
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017 numpy-images.zip - 7.27 MB
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005 NumPy DataTypes and Attributes_en.srt - 20.04 KB
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014 Exercise Nut Butter Store Sales_en.srt - 17.41 KB
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009 Manipulating Arrays_en.srt - 17.15 KB
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013 Dot Product vs Element Wise_en.srt - 15.89 KB
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008 Viewing Arrays and Matrices_en.srt - 13.86 KB
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006 Creating NumPy Arrays_en.srt - 12.46 KB
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010 Manipulating Arrays 2_en.srt - 12.01 KB
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017 Turn Images Into NumPy Arrays_en.srt - 10.6 KB
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007 NumPy Random Seed_en.srt - 10.45 KB
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011 Standard Deviation and Variance_en.srt - 9.82 KB
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012 Reshape and Transpose_en.srt - 9.68 KB
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016 Sorting Arrays_en.srt - 8.95 KB
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003 NumPy Introduction_en.srt - 7.6 KB
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015 Comparison Operators_en.srt - 5.22 KB
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002 Section Overview_en.srt - 3.25 KB
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018 Assignment NumPy Practice.html - 2.17 KB
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004 Quick Note Correction In Next Video.html - 1.24 KB
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external-assets-links.txt - 1.1 KB
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019 Optional Extra NumPy resources.html - 1.02 KB
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001 Quick Note Upcoming Videos.html - 706 B
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13 - Passing the TensorFlow Developer Certificate Exam
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004 How to prepare (your brain) for the TensorFlow Developer Certification.mp4 - 80.19 MB
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002 What is the TensorFlow Developer Certification.mp4 - 53.99 MB
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005 How to prepare (your computer) for the TensorFlow Developer Certification.mp4 - 45.83 MB
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003 Why the TensorFlow Developer Certification.mp4 - 34.36 MB
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006 What to do after the TensorFlow Developer Certification exam.mp4 - 16.58 MB
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005 How to prepare (your computer) for the TensorFlow Developer Certification_en.srt - 20.49 KB
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004 How to prepare (your brain) for the TensorFlow Developer Certification_en.srt - 13.01 KB
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003 Why the TensorFlow Developer Certification_en.srt - 11.37 KB
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002 What is the TensorFlow Developer Certification_en.srt - 8.43 KB
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006 What to do after the TensorFlow Developer Certification exam_en.srt - 3.92 KB
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001 Get ready to be TensorFlow Developer Certified!.html - 1.63 KB
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01 - Introduction
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002 Join Our Online Classroom!.mp4 - 77.58 MB
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001 Course Outline.mp4 - 58.04 MB
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001 Course Outline_en.srt - 7.97 KB
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002 Join Our Online Classroom!_en.srt - 6 KB
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003 Exercise Meet Your Classmates and Instructor.html - 3.7 KB
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004 All Course Resources + Notebooks.html - 2.53 KB
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005 Python + Machine Learning Monthly.html - 796 B
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external-assets-links.txt - 186 B
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15 - Appendix Machine Learning Primer
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004 Exercise Machine Learning Playground.mp4 - 35.79 MB
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005 How Did We Get Here.mp4 - 24.98 MB
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002 What is Machine Learning.mp4 - 18.2 MB
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003 AIMachine LearningData Science.mp4 - 13.23 MB
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009 What Is Machine Learning Round 2.mp4 - 11.85 MB
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007 Types of Machine Learning.mp4 - 9.63 MB
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006 Exercise YouTube Recommendation Engine.mp4 - 8.89 MB
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010 Section Review.mp4 - 2.81 MB
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002 What is Machine Learning_en.srt - 8.96 KB
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004 Exercise Machine Learning Playground_en.srt - 8.13 KB
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005 How Did We Get Here_en.srt - 7.34 KB
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003 AIMachine LearningData Science_en.srt - 6.45 KB
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009 What Is Machine Learning Round 2_en.srt - 6.25 KB
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006 Exercise YouTube Recommendation Engine_en.srt - 5.62 KB
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007 Types of Machine Learning_en.srt - 5.51 KB
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010 Section Review_en.srt - 2.2 KB
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001 Quick Note Upcoming Videos.html - 706 B
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008 Are You Getting It Yet.html - 160 B
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external-assets-links.txt - 149 B
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16 - Appendix Machine Learning and Data Science Framework
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005 Types of Machine Learning Problems.mp4 - 20.98 MB
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006 Types of Data.mp4 - 20.01 MB
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012 Modelling - Comparison.mp4 - 18.23 MB
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008 Features In Data.mp4 - 17.38 MB
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015 Tools We Will Use.mp4 - 12.91 MB
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014 Experimentation.mp4 - 11.54 MB
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009 Modelling - Splitting Data.mp4 - 11.31 MB
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010 Modelling - Picking the Model.mp4 - 8.74 MB
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004 6 Step Machine Learning Framework.mp4 - 8.42 MB
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007 Types of Evaluation.mp4 - 6.52 MB
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002 Section Overview.mp4 - 6.34 MB
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011 Modelling - Tuning.mp4 - 6.16 MB
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003 Introducing Our Framework.mp4 - 4.29 MB
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005 Types of Machine Learning Problems_en.srt - 14.46 KB
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012 Modelling - Comparison_en.srt - 13.32 KB
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009 Modelling - Splitting Data_en.srt - 7.8 KB
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008 Features In Data_en.srt - 6.88 KB
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004 6 Step Machine Learning Framework_en.srt - 6.86 KB
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006 Types of Data_en.srt - 6.49 KB
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010 Modelling - Picking the Model_en.srt - 6.24 KB
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015 Tools We Will Use_en.srt - 6.08 KB
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011 Modelling - Tuning_en.srt - 5.09 KB
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014 Experimentation_en.srt - 5.09 KB
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002 Section Overview_en.srt - 4.79 KB
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007 Types of Evaluation_en.srt - 4.56 KB
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003 Introducing Our Framework_en.srt - 3.7 KB
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013 Overfitting and Underfitting Definitions.html - 1.95 KB
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016 Optional Elements of AI.html - 975 B
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001 Quick Note Upcoming Videos.html - 706 B
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external-assets-links.txt - 108 B
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14 - Where To Go From Here
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002 LinkedIn Endorsements.html - 1.37 KB
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001 Become An Alumni.html - 944 B
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003 TensorFlow Certificate.html - 385 B
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19 - BONUS SECTION
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001 Special Bonus Lecture.html - 1.19 KB
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0. Websites you may like
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[FreeCourseSite.com].url - 127 B
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[CourseClub.Me].url - 122 B
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[GigaCourse.Com].url - 49 B
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InfoHash:
D6D66D3B6FF225DC20AAC19BC7F900FB80594A84
Last updated:
Jul 18, 2022
File count:
954 B
File Category:
1
Tags:
freecoursesite, udemy, tensorflow, developer, certificate, zero, mastery