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Torrent Details
Coursera_Neural-Networks-and-Machine-Learning_Geoffrey-Hinton_University-of-Toronto
Category:
Other
Size:
532.59 MB
Seeders:
6
Leechers:
1
Downloads:
25
Uploaded:
Nov 6, 2018
Torrent
Magnet
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Coursera_Neural-Networks-and-Machine-Learning_Geoffrey-Hinton_University-of-Toronto
1 - 1 - Why do we need machine learning [13 min].mp4
- 15.05 MB
1 - 2 - What are neural networks [8 min].mp4
- 9.76 MB
1 - 3 - Some simple models of neurons [8 min].mp4
- 9.26 MB
1 - 4 - A simple example of learning [6 min].mp4
- 6.57 MB
1 - 5 - Three types of learning [8 min].mp4
- 8.96 MB
10 - 1 - Why it helps to combine models [13 min].mp4
- 15.12 MB
10 - 2 - Mixtures of Experts [13 min].mp4
- 14.98 MB
10 - 3 - The idea of full Bayesian learning [7 min].mp4
- 8.39 MB
10 - 4 - Making full Bayesian learning practical [7 min].mp4
- 8.13 MB
10 - 5 - Dropout [9 min].mp4
- 9.69 MB
2 - 1 - Types of neural network architectures [7 min].mp4
- 8.78 MB
2 - 2 - Perceptrons The first generation of neural networks [8 min].mp4
- 9.39 MB
2 - 3 - A geometrical view of perceptrons [6 min].mp4
- 7.32 MB
2 - 4 - Why the learning works [5 min].mp4
- 5.9 MB
2 - 5 - What perceptrons cant do [15 min].mp4
- 16.57 MB
3 - 1 - Learning the weights of a linear neuron [12 min].mp4
- 13.52 MB
3 - 2 - The error surface for a linear neuron [5 min].mp4
- 5.89 MB
3 - 3 - Learning the weights of a logistic output neuron [4 min].mp4
- 4.37 MB
3 - 4 - The backpropagation algorithm [12 min].mp4
- 13.35 MB
3 - 5 - Using the derivatives computed by backpropagation [10 min].mp4
- 11.15 MB
4 - 1 - Learning to predict the next word [13 min].mp4
- 14.28 MB
4 - 2 - A brief diversion into cognitive science [4 min].mp4
- 5.31 MB
4 - 3 - Another diversion The softmax output function [7 min].mp4
- 8.03 MB
4 - 4 - Neuro-probabilistic language models [8 min].mp4
- 8.93 MB
4 - 5 - Ways to deal with the large number of possible outputs [15 min].mp4
- 14.26 MB
5 - 1 - Why object recognition is difficult [5 min].mp4
- 5.37 MB
5 - 2 - Achieving viewpoint invariance [6 min].mp4
- 6.89 MB
5 - 3 - Convolutional nets for digit recognition [16 min].mp4
- 18.46 MB
5 - 4 - Convolutional nets for object recognition [17min].mp4
- 23.03 MB
6 - 1 - Overview of mini-batch gradient descent.mp4
- 9.6 MB
6 - 2 - A bag of tricks for mini-batch gradient descent.mp4
- 14.9 MB
6 - 3 - The momentum method.mp4
- 9.74 MB
6 - 4 - Adaptive learning rates for each connection.mp4
- 6.63 MB
6 - 5 - Rmsprop Divide the gradient by a running average of its recent magnitude.mp4
- 15.12 MB
7 - 1 - Modeling sequences A brief overview.mp4
- 20.13 MB
7 - 2 - Training RNNs with back propagation.mp4
- 7.33 MB
7 - 3 - A toy example of training an RNN.mp4
- 7.24 MB
7 - 4 - Why it is difficult to train an RNN.mp4
- 8.89 MB
7 - 5 - Long-term Short-term-memory.mp4
- 10.23 MB
8 - 1 - A brief overview of Hessian Free optimization.mp4
- 16.24 MB
8 - 2 - Modeling character strings with multiplicative connections [14 mins].mp4
- 16.56 MB
8 - 3 - Learning to predict the next character using HF [12 mins].mp4
- 13.92 MB
8 - 4 - Echo State Networks [9 min].mp4
- 11.28 MB
9 - 1 - Overview of ways to improve generalization [12 min].mp4
- 13.57 MB
9 - 2 - Limiting the size of the weights [6 min].mp4
- 7.36 MB
9 - 3 - Using noise as a regularizer [7 min].mp4
- 8.48 MB
9 - 4 - Introduction to the full Bayesian approach [12 min].mp4
- 12 MB
9 - 5 - The Bayesian interpretation of weight decay [11 min].mp4
- 12.27 MB
9 - 6 - MacKays quick and dirty method of setting weight costs [4 min].mp4
- 4.37 MB
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udp://tracker.opentrackr.org:1337/announce
5
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udp://tracker.moeking.me:6969/announce
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udp://open.stealth.si:80/announce
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udp://tracker.openbittorrent.com:80/announce
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udp://tracker4.itzmx.com:2710/announce
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InfoHash:
BA102098008A21226094AFEC8A2C6A7F25276E5C
Last updated:
Jan 24, 2022
File count:
49 B
File Category:
1
Tags:
networks, machine, toronto
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