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GetFreeCourses.Co-Udemy-PyTorch for Deep Learning in 2023 Zero to Mastery
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5. PyTorch Computer Vision
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22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.mp4 - 208.33 MB
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25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.mp4 - 174.82 MB
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23. Model 2 Breaking Down Conv2D Step by Step.mp4 - 162.72 MB
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31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.mp4 - 160.84 MB
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24. Model 2 Breaking Down MaxPool2D Step by Step.mp4 - 158.11 MB
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12. Writing Training and Testing Loops for Our Batched Data.mp4 - 157.56 MB
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5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.mp4 - 154 MB
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9. Model 0 Creating a Baseline Model with Two Linear Layers.mp4 - 136.88 MB
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1. What Is a Computer Vision Problem and What We Are Going to Cover.mp4 - 113.67 MB
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10. Creating a Loss Function an Optimizer for Model 0.mp4 - 110.54 MB
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19. Training and Testing Model 1 with Our Training and Testing Functions.mp4 - 108.44 MB
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13. Writing an Evaluation Function to Get Our Models Results.mp4 - 106.79 MB
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8. Turning Our Datasets Into DataLoaders.mp4 - 100.24 MB
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33. Saving and Loading Our Best Performing Model.mp4 - 98.16 MB
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21. Model 2 Convolutional Neural Networks High Level Overview.mp4 - 94.63 MB
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4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.mp4 - 89.2 MB
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15. Model 1 Creating a Model with Non-Linear Functions.mp4 - 86.39 MB
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2. Computer Vision Input and Output Shapes.mp4 - 85.02 MB
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29. Making Predictions on Random Test Samples with the Best Trained Model.mp4 - 83.66 MB
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34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.mp4 - 81.9 MB
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27. Model 2 Training Our First CNN and Evaluating Its Results.mp4 - 76.79 MB
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17. Turing Our Training Loop into a Function.mp4 - 70.89 MB
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6. Visualizing Random Samples of Data.mp4 - 68.11 MB
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32. Evaluating Our Best Models Predictions with a Confusion Matrix.mp4 - 67.01 MB
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30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.mp4 - 63.49 MB
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28. Comparing the Results of Our Modelling Experiments.mp4 - 61.76 MB
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7. DataLoader Overview Understanding Mini-Batches.mp4 - 60.21 MB
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3. What Is a Convolutional Neural Network (CNN).mp4 - 55.4 MB
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18. Turing Our Testing Loop into a Function.mp4 - 50.89 MB
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11. Creating a Function to Time Our Modelling Code.mp4 - 45.61 MB
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14. Setup Device-Agnostic Code for Running Experiments on the GPU.mp4 - 44.32 MB
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20. Getting a Results Dictionary for Model 1.mp4 - 41.35 MB
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16. Mode 1 Creating a Loss Function and Optimizer.mp4 - 31.34 MB
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26. Model 2 Setting Up a Loss Function and Optimizer.mp4 - 27.88 MB
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10. PyTorch Paper Replicating
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37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces.mp4 - 190.82 MB
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36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.mp4 - 188.75 MB
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23. Creating the Patch Embedding Layer with PyTorch.mp4 - 170.03 MB
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44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.mp4 - 164.75 MB
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30. Turning Equation 2 into Code.mp4 - 163.87 MB
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16. Calculating the Input and Output Shape of the Embedding Layer by Hand.mp4 - 160.6 MB
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17. Turning a Single Image into Patches (Part 1 Patching the Top Row).mp4 - 150.16 MB
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28. Equation 2 Multihead Attention Overview.mp4 - 144.11 MB
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19. Creating Patch Embeddings with a Convolutional Layer.mp4 - 142.63 MB
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11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.mp4 - 140.93 MB
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27. Equation 1 Putting it All Together.mp4 - 134.82 MB
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24. Creating the Class Token Embedding.mp4 - 131.99 MB
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25. Creating the Class Token Embedding - Less Birds.mp4 - 131.91 MB
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18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).mp4 - 130.64 MB
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20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.mp4 - 129.06 MB
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13. Breaking Down Equation 2 and 3.mp4 - 125.04 MB
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15. Breaking Down Table 1.mp4 - 122.08 MB
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40. Creating a Loss Function and Optimizer from the ViT Paper.mp4 - 118.33 MB
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29. Equation 2 Layernorm Overview.mp4 - 111.76 MB
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38. Bringing Our Own Vision Transformer to Life - Part 2 The Forward Method.mp4 - 111.37 MB
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3. Where Can You Find Machine Learning Research Papers and Code.mp4 - 110.75 MB
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26. Creating the Position Embedding.mp4 - 109.18 MB
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33. Turning Equation 3 into Code.mp4 - 107.07 MB
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12. Breaking Down Equation 1.mp4 - 103.22 MB
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42. Discussing what Our Training Setup Is Missing.mp4 - 101.2 MB
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5. Getting Setup for Coding in Google Colab.mp4 - 99.14 MB
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1. What Is a Machine Learning Research Paper.mp4 - 93.94 MB
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14. Breaking Down Equation 4.mp4 - 92.44 MB
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7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.mp4 - 89.7 MB
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21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.mp4 - 89.61 MB
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32. Equation 3 Replication Overview.mp4 - 88.7 MB
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4. What We Are Going to Cover.mp4 - 87.76 MB
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10. Breaking Down Figure 1 of the ViT Paper.mp4 - 87.12 MB
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50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.mp4 - 85.49 MB
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39. Getting a Visual Summary of Our Custom Vision Transformer.mp4 - 84.89 MB
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35. Combining equation 2 and 3 to Create the Transformer Encoder.mp4 - 84.87 MB
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34. Transformer Encoder Overview.mp4 - 82.85 MB
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9. Replicating a Vision Transformer - High Level Overview.mp4 - 77.84 MB
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46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.mp4 - 76.29 MB
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43. Plotting a Loss Curve for Our ViT Model.mp4 - 63.4 MB
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45. Preparing Data to Be Used with a Pretrained ViT.mp4 - 57.22 MB
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31. Checking the Inputs and Outputs of Equation.mp4 - 53.69 MB
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41. Training our Custom ViT on Food Vision Mini.mp4 - 53.48 MB
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22. Visualizing a Single Sequence Vector of Patch Embeddings.mp4 - 50.37 MB
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6. Downloading Data for Food Vision Mini.mp4 - 43.83 MB
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48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.mp4 - 41.81 MB
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47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.mp4 - 40.36 MB
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49. Making Predictions on a Custom Image with Our Pretrained ViT.mp4 - 37.11 MB
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8. Visualizing a Single Image.mp4 - 36.44 MB
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2. Why Replicate a Machine Learning Research Paper.mp4 - 23.26 MB
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11. PyTorch Model Deployment
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23. Creating a Function to Make and Time Predictions with Our Models.mp4 - 185.78 MB
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49. Training Food Vision Big Our Biggest Model Yet!.mp4 - 184.22 MB
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56. Deploying Food Vision Big to Hugging Face Spaces.mp4 - 162.53 MB
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42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.mp4 - 143.59 MB
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3. Where Is My Model Going to Go.mp4 - 139.84 MB
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39. Turning Our Food Vision Mini Demo App Into a Python Script.mp4 - 137.63 MB
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32. Bringing Food Vision Mini to Life in a Live Web Application.mp4 - 135.39 MB
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27. Visualizing the Performance vs Speed Trade-off.mp4 - 134.67 MB
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47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.mp4 - 119.74 MB
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41. Downloading Our Food Vision Mini App Files from Google Colab.mp4 - 112.22 MB
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54. Creating an App Script for Our Food Vision Big Model Gradio Demo.mp4 - 104.81 MB
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24. Making and Timing Predictions with EffNetB2.mp4 - 97.63 MB
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13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.mp4 - 97.04 MB
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45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.mp4 - 96.53 MB
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30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.mp4 - 95.22 MB
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28. Gradio Overview and Installation.mp4 - 95.13 MB
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22. Outlining the Steps for Making and Timing Predictions for Our Models.mp4 - 93.42 MB
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36. Creating an Examples Directory with Example Food Vision Mini Images.mp4 - 92.41 MB
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10. Creating an EffNetB2 Feature Extractor Model.mp4 - 92.12 MB
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43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.mp4 - 91.61 MB
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26. Comparing EffNetB2 and ViT Model Statistics.mp4 - 89.62 MB
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34. Outlining the File Structure of Our Deployed App.mp4 - 89.54 MB
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57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.mp4 - 81.75 MB
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29. Gradio Function Outline.mp4 - 79.9 MB
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17. Creating a Vision Transformer Feature Extractor Model.mp4 - 78.51 MB
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1. What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.mp4 - 73.84 MB
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25. Making and Timing Predictions with ViT.mp4 - 72.47 MB
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37. Writing Code to Move Our Saved EffNetB2 Model File.mp4 - 71.91 MB
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46. Downloading the Food 101 Dataset.mp4 - 71.67 MB
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4. How Is My Model Going to Function.mp4 - 67.36 MB
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52. Saving Food 101 Class Names to a Text File and Reading them Back In.mp4 - 66.81 MB
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5. Some Tools and Places to Deploy Machine Learning Models.mp4 - 65.36 MB
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33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.mp4 - 64.81 MB
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16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.mp4 - 63.27 MB
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7. Getting Setup to Code.mp4 - 62.88 MB
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19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.mp4 - 62 MB
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48. Turning Our Food 101 Datasets into DataLoaders.mp4 - 61.5 MB
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9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.mp4 - 58.56 MB
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11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.mp4 - 57.6 MB
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15. Getting the Size of Our EffNetB2 Model in Megabytes.mp4 - 55.48 MB
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31. Creating a List of Examples to Pass to Our Gradio Demo.mp4 - 53.31 MB
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50. Outlining the File Structure for Our Food Vision Big.mp4 - 52.78 MB
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2. Three Questions to Ask for Machine Learning Model Deployment.mp4 - 46.93 MB
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21. Collecting Stats About Our-ViT Feature Extractor.mp4 - 45.86 MB
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38. Turning Our EffNetB2 Model Creation Function Into a Python Script.mp4 - 44.78 MB
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20. Saving Our ViT Feature Extractor and Inspecting Its Size.mp4 - 43.77 MB
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6. What We Are Going to Cover.mp4 - 40.83 MB
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55. Zipping and Downloading Our Food Vision Big App Files.mp4 - 39.76 MB
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8. Downloading a Dataset for Food Vision Mini.mp4 - 39.25 MB
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44. Food Vision Big Project Outline.mp4 - 39.15 MB
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35. Creating a Food Vision Mini Demo Directory to House Our App Files.mp4 - 39.14 MB
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40. Creating a Requirements File for Our Food Vision Mini App.mp4 - 37.5 MB
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51. Downloading an Example Image and Moving Our Food Vision Big Model File.mp4 - 36.59 MB
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12. Creating DataLoaders for EffNetB2.mp4 - 31.38 MB
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14. Saving Our EffNetB2 Model to File.mp4 - 26.71 MB
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53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.mp4 - 23.9 MB
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18. Creating DataLoaders for Our ViT Feature Extractor Model.mp4 - 19.7 MB
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Download Paid Udemy Courses For Free.url - 116 B
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GetFreeCourses.Co.url - 116 B
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3. PyTorch Workflow
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15. Reviewing the Steps in a Training Loop Step by Step.mp4 - 177.46 MB
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18. Reviewing What Happens in a Testing Loop Step by Step.mp4 - 161.56 MB
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17. Writing Testing Loop Code and Discussing What's Happening Step by Step.mp4 - 135.03 MB
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6. Creating Our First PyTorch Model for Linear Regression.mp4 - 130.08 MB
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19. Writing Code to Save a PyTorch Model.mp4 - 129.82 MB
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13. PyTorch Training Loop Steps and Intuition.mp4 - 128.78 MB
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12. Setting Up an Optimizer and a Loss Function.mp4 - 116 MB
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10. Making Predictions With Our Random Model Using Inference Mode.mp4 - 107.03 MB
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24. Putting Everything Together (Part 3) Training a Model.mp4 - 103 MB
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9. Checking Out the Internals of Our PyTorch Model.mp4 - 102.71 MB
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16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.mp4 - 101.7 MB
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23. Putting Everything Together (Part 2) Building a Model.mp4 - 88.7 MB
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14. Writing Code for a PyTorch Training Loop.mp4 - 83 MB
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20. Writing Code to Load a PyTorch Model.mp4 - 79.58 MB
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8. Discussing Some of the Most Important PyTorch Model Building Classes.mp4 - 74.44 MB
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26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.mp4 - 72.52 MB
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2. Getting Setup and What We Are Covering.mp4 - 69.67 MB
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11. Training a Model Intuition (The Things We Need).mp4 - 69.5 MB
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3. Creating a Simple Dataset Using the Linear Regression Formula.mp4 - 68.65 MB
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4. Splitting Our Data Into Training and Test Sets.mp4 - 65.22 MB
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7. Breaking Down What's Happening in Our PyTorch Linear regression Model.mp4 - 62.18 MB
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5. Building a function to Visualize Our Data.mp4 - 61.89 MB
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25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.mp4 - 50.63 MB
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22. Putting Everything Together (Part 1) Data.mp4 - 49.35 MB
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28. PyTorch Workflow Exercises and Extra-Curriculum.mp4 - 49.32 MB
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21. Setting Up to Practice Everything We Have Done Using Device Agnostic code.mp4 - 45.8 MB
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27. Exercise Imposter Syndrome.mp4 - 39.25 MB
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1. Introduction and Where You Can Get Help.mp4 - 28.6 MB
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6. PyTorch Custom Datasets
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14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.mp4 - 176.28 MB
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18. Exploring State of the Art Data Augmentation With Torchvision Transforms.mp4 - 166.35 MB
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3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.mp4 - 150.96 MB
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27. The Balance Between Overfitting and Underfitting and How to Deal With Each.mp4 - 131.82 MB
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16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.mp4 - 131.22 MB
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8. Transforming Data (Part 2) Visualizing Transformed Images.mp4 - 127.58 MB
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34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.mp4 - 127.06 MB
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20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.mp4 - 117.23 MB
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5. Becoming One With the Data (Part 2) Visualizing a Random Image.mp4 - 115.34 MB
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36. Predicting on Custom Data (Part 5) Putting It All Together.mp4 - 113.03 MB
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23. Creating Training and Testing loop Functions.mp4 - 106.17 MB
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24. Creating a Train Function to Train and Evaluate Our Models.mp4 - 103.47 MB
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28. Creating Augmented Training Datasets and DataLoaders for Model 1.mp4 - 98.83 MB
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9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.mp4 - 98.17 MB
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21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.mp4 - 96.5 MB
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1. What Is a Custom Dataset and What We Are Going to Cover.mp4 - 92.59 MB
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26. Plotting the Loss Curves of Model 0.mp4 - 89.45 MB
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25. Training and Evaluating Model 0 With Our Training Functions.mp4 - 89.28 MB
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31. Plotting the Loss Curves of All of Our Models Against Each Other.mp4 - 89.27 MB
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4. Becoming One With the Data (Part 1) Exploring the Data Format.mp4 - 87.61 MB
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11. Turning Our Image Datasets into PyTorch Dataloaders.mp4 - 84.33 MB
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7. Transforming Data (Part 1) Turning Images Into Tensors.mp4 - 81.72 MB
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17. Turning Our Custom Datasets Into DataLoaders.mp4 - 80.62 MB
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13. Creating a Helper Function to Get Class Names From a Directory.mp4 - 79.09 MB
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19. Building a Baseline Model (Part 1) Loading and Transforming Data.mp4 - 77.93 MB
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10. Visualizing a Loaded Image From the Train Dataset.mp4 - 76.73 MB
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12. Creating a Custom Dataset Class in PyTorch High Level Overview.mp4 - 74.7 MB
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37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.mp4 - 73.32 MB
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15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.mp4 - 69.5 MB
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33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.mp4 - 67.99 MB
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22. Using the Torchinfo Package to Get a Summary of Our Model.mp4 - 64.97 MB
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29. Constructing and Training Model 1.mp4 - 60.65 MB
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6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.mp4 - 51.91 MB
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32. Predicting on Custom Data (Part 1) Downloading an Image.mp4 - 51.66 MB
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2. Importing PyTorch and Setting Up Device Agnostic Code.mp4 - 48.97 MB
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35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.mp4 - 36.07 MB
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30. Plotting the Loss Curves of Model 1.mp4 - 31.69 MB
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Download Paid Udemy Courses For Free.url - 116 B
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GetFreeCourses.Co.url - 116 B
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7. PyTorch Going Modular
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9. Creating a Training Script to Train Our Model in One Line of Code.mp4 - 165.52 MB
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4. Writing the Outline for Our First Python Script to Setup the Data.mp4 - 156.79 MB
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5. Creating a Python Script to Create Our PyTorch DataLoaders.mp4 - 135.14 MB
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6. Turning Our Model Building Code into a Python Script.mp4 - 115.13 MB
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2. Going Modular Notebook (Part 1) Running It End to End.mp4 - 104.92 MB
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1. What Is Going Modular and What We Are Going to Cover.mp4 - 100.12 MB
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10. Going Modular Summary, Exercises and Extra-Curriculum.mp4 - 80.67 MB
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7. Turning Our Model Training Code into a Python Script.mp4 - 80 MB
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8. Turning Our Utility Function to Save a Model into a Python Script.mp4 - 75.79 MB
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3. Downloading a Dataset.mp4 - 67.64 MB
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4. PyTorch Neural Network Classification
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10. Loss Function Optimizer and Evaluation Function for Our Classification Network.mp4 - 161.06 MB
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22. Writing Training and Testing Code for Our First Non-Linear Model.mp4 - 150.57 MB
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29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.mp4 - 150.09 MB
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13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.mp4 - 149.99 MB
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11. Going from Model Logits to Prediction Probabilities to Prediction Labels.mp4 - 134.54 MB
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12. Coding a Training and Testing Optimization Loop for Our Classification Model.mp4 - 126.75 MB
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9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.mp4 - 123.24 MB
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16. Writing Training and Testing Code to See if Our Upgraded Model Performs Better.mp4 - 118.64 MB
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26. Creating a Multi-Class Classification Model with PyTorch.mp4 - 107.44 MB
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31. Discussing a Few More Classification Metrics.mp4 - 97.54 MB
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25. Putting It All Together (Part 1) Building a Multiclass Dataset.mp4 - 97.46 MB
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28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.mp4 - 97.05 MB
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20. Introducing the Missing Piece for Our Classification Model Non-Linearity.mp4 - 96.51 MB
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21. Building Our First Neural Network with Non-Linearity.mp4 - 92.59 MB
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4. Making a Toy Classification Dataset.mp4 - 91.48 MB
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8. Making Our Neural Network Visual.mp4 - 91.27 MB
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7. Coding a Small Neural Network to Handle Our Classification Data.mp4 - 86.85 MB
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1. Introduction to Machine Learning Classification With PyTorch.mp4 - 84.58 MB
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5. Turning Our Data into Tensors and Making a Training and Test Split.mp4 - 81.06 MB
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14. Discussing Options to Improve a Model.mp4 - 80.87 MB
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24. Replicating Non-Linear Activation Functions with Pure PyTorch.mp4 - 80.74 MB
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30. Making Predictions with and Evaluating Our Multi-Class Classification Model.mp4 - 77.05 MB
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18. Building and Training a Model to Fit on Straight Line Data.mp4 - 71.67 MB
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15. Creating a New Model with More Layers and Hidden Units.mp4 - 68.81 MB
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3. Typical Architecture of a Classification Neural Network (Overview).mp4 - 67.05 MB
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27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.mp4 - 65.06 MB
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17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.mp4 - 61.36 MB
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23. Making Predictions with and Evaluating Our First Non-Linear Model.mp4 - 53.05 MB
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19. Evaluating Our Models Predictions on Straight Line Data.mp4 - 50.8 MB
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2. Classification Problem Example Input and Output Shapes.mp4 - 49.97 MB
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32. PyTorch Classification Exercises and Extra-Curriculum.mp4 - 41.47 MB
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6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.mp4 - 31.92 MB
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8. PyTorch Transfer Learning
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12. Freezing the Base Layers of Our Model and Updating the Classifier Head.mp4 - 160.67 MB
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6. Turning Our Data into DataLoaders with Manually Created Transforms.mp4 - 141.48 MB
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7. Turning Our Data into DataLoaders with Automatic Created Transforms.mp4 - 139.74 MB
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8. Which Pretrained Model Should You Use.mp4 - 128.78 MB
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9. Setting Up a Pretrained Model with Torchvision.mp4 - 113.15 MB
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16. Creating a Function Predict On and Plot Images.mp4 - 101.67 MB
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1. Introduction What is Transfer Learning and Why Use It.mp4 - 97.26 MB
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4. Downloading Our Previously Written Code from Going Modular.mp4 - 83.75 MB
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3. Installing the Latest Versions of Torch and Torchvision.mp4 - 82.39 MB
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17. Making and Plotting Predictions on Test Images.mp4 - 78.14 MB
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11. Getting a Summary of the Different Layers of Our Model.mp4 - 76.04 MB
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13. Training Our First Transfer Learning Feature Extractor Model.mp4 - 74.81 MB
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5. Downloading Pizza, Steak, Sushi Image Data from Github.mp4 - 72.17 MB
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18. Making a Prediction on a Custom Image.mp4 - 67.83 MB
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15. Outlining the Steps to Make Predictions on the Test Images.mp4 - 66.74 MB
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14. Plotting the Loss curves of Our Transfer Learning Model.mp4 - 58.93 MB
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10. Different Kinds of Transfer Learning.mp4 - 56.96 MB
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2. Where Can You Find Pretrained Models and What We Are Going to Cover.mp4 - 55.85 MB
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19. Main Takeaways, Exercises and Extra- Curriculum.mp4 - 44.43 MB
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9. PyTorch Experiment Tracking
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16. Creating Functions to Prepare Our Feature Extractor Models.mp4 - 159.21 MB
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7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.mp4 - 150.28 MB
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19. Viewing Our Modelling Experiments in TensorBoard.mp4 - 140.3 MB
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17. Coding Out the Steps to Run a Series of Modelling Experiments.mp4 - 127.62 MB
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9. Exploring Our Single Models Results with TensorBoard.mp4 - 116.26 MB
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6. Preparing a Pretrained Model for Our Own Problem.mp4 - 113.16 MB
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20. Loading the Best Model and Making Predictions on Random Images from the Test Set.mp4 - 99.19 MB
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3. Creating a Function to Download Data.mp4 - 95.23 MB
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2. Getting Setup by Importing Torch Libraries and Going Modular Code.mp4 - 93.39 MB
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4. Turning Our Data into DataLoaders Using Manual Transforms.mp4 - 92.72 MB
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5. Turning Our Data into DataLoaders Using Automatic Transforms.mp4 - 82.01 MB
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10. Creating a Function to Create SummaryWriter Instances.mp4 - 80.1 MB
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15. Turning Our Datasets into DataLoaders Ready for Experimentation.mp4 - 78.07 MB
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11. Adapting Our Train Function to Be Able to Track Multiple Experiments.mp4 - 66.54 MB
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14. Downloading Datasets for Our Modelling Experiments.mp4 - 66.42 MB
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1. What Is Experiment Tracking and Why Track Experiments.mp4 - 61.86 MB
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13. Discussing the Experiments We Are Going to Try.mp4 - 48.3 MB
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12. What Experiments Should You Try.mp4 - 46.92 MB
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18. Running Eight Different Modelling Experiments in 5 Minutes.mp4 - 45.66 MB
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22. Main Takeaways, Exercises and Extra- Curriculum.mp4 - 43.59 MB
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8. Training a Single Model and Saving the Results to TensorBoard.mp4 - 41.79 MB
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21. Making a Prediction on Our Own Custom Image with the Best Model.mp4 - 39.71 MB
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2. PyTorch Fundamentals
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7. What Is and Why PyTorch.mp4 - 113.56 MB
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30. Different Ways of Accessing a GPU in PyTorch.mp4 - 113.01 MB
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25. Reshaping, Viewing and Stacking Tensors.mp4 - 103.95 MB
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22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.mp4 - 97.35 MB
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29. PyTorch Reproducibility (Taking the Random Out of Random).mp4 - 95.11 MB
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13. Introduction to PyTorch Tensors.mp4 - 94 MB
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26. Squeezing, Unsqueezing and Permuting Tensors.mp4 - 88.41 MB
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14. Creating Random Tensors in PyTorch.mp4 - 86.42 MB
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17. Dealing With Tensor Data Types.mp4 - 81.4 MB
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20. Matrix Multiplication (Part 1).mp4 - 77.8 MB
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4. Anatomy of Neural Networks.mp4 - 70.32 MB
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12. Getting Setup to Write PyTorch Code.mp4 - 70 MB
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18. Getting Tensor Attributes.mp4 - 66.44 MB
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31. Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.mp4 - 64.51 MB
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28. PyTorch Tensors and NumPy.mp4 - 59.78 MB
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11. Important Resources For This Course.mp4 - 58.31 MB
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21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.mp4 - 57.78 MB
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27. Selecting Data From Tensors (Indexing).mp4 - 56.96 MB
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32. PyTorch Fundamentals Exercises and Extra-Curriculum.mp4 - 56.76 MB
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3. Machine Learning vs. Deep Learning.mp4 - 55.3 MB
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9. What We Are Going To Cover With PyTorch.mp4 - 50.45 MB
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23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).mp4 - 48.14 MB
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6. What Can Deep Learning Be Used For.mp4 - 43.2 MB
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19. Manipulating Tensors (Tensor Operations).mp4 - 39.7 MB
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10. How To and How Not To Approach This Course.mp4 - 37.74 MB
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2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.mp4 - 35.34 MB
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16. Creating a Tensor Range and Tensors Like Other Tensors.mp4 - 32.59 MB
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5. Different Types of Learning Paradigms.mp4 - 27.05 MB
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8. What Are Tensors.mp4 - 24.99 MB
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15. Creating Tensors With Zeros and Ones in PyTorch.mp4 - 24.56 MB
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24. Finding The Positional Min and Max of Tensors.mp4 - 24.5 MB
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1. Why Use Machine Learning or Deep Learning.mp4 - 13.8 MB
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33. Unlimited Updates.html - 1.68 KB
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Download Paid Udemy Courses For Free.url - 116 B
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1. Introduction
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3. Join Our Online Classroom!.mp4 - 75.35 MB
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1. PyTorch for Deep Learning.mp4 - 75.35 MB
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2. Course Welcome and What Is Deep Learning.mp4 - 38.99 MB
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4. Exercise Meet Your Classmates + Instructor.html - 3.79 KB
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5. Course Companion Book + Code + More.html - 1.1 KB
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6. Machine Learning + Python Monthly Newsletters.html - 870 B
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12. Where To Go From Here
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1. Thank You!.mp4 - 20.99 MB
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Download Paid Udemy Courses For Free.url - 116 B
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GetFreeCourses.Co.url - 116 B
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InfoHash:
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Last updated:
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