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- videos/Neural Networks for Machine Learning 13.4 OPTIONAL VIDEO RBMs are infinite sigmoid belief nets.mp4 20.4 MB
- videos/Neural Networks for Machine Learning 4.2 Convolutional nets for digit recognition.mp4 19.4 MB
- videos/Neural Networks for Machine Learning 11.1 OPTIONAL VIDEO More efficient ways to get the statistics.mp4 17.8 MB
- videos/Neural Networks for Machine Learning 1.4 What perceptrons can't do.mp4 17.4 MB
- videos/Neural Networks for Machine Learning 7.1 Modeling character strings with multiplicative connections.mp4 17.4 MB
- videos/Neural Networks for Machine Learning 7.0 A brief overview of Hessian Free optimization.mp4 17.0 MB
- videos/Neural Networks for Machine Learning 15.2 OPTIONAL Bayesian optimization of hyper-parameters.mp4 16.6 MB
- videos/Neural Networks for Machine Learning 12.3 The wake-sleep algorithm.mp4 16.4 MB
- videos/Neural Networks for Machine Learning 9.0 Why it helps to combine models.mp4 15.9 MB
- videos/Neural Networks for Machine Learning 5.4 Rmsprop Divide the gradient by a running average of its recent magnitude.mp4 15.9 MB
- videos/Neural Networks for Machine Learning 0.0 Why do we need machine learning.mp4 15.8 MB
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- videos/Neural Networks for Machine Learning 5.1 A bag of tricks for mini-batch gradient descent.mp4 15.6 MB
- videos/Neural Networks for Machine Learning 12.1 Belief Nets.mp4 15.6 MB
- videos/Neural Networks for Machine Learning 10.0 Hopfield Nets.mp4 15.4 MB
- videos/Neural Networks for Machine Learning 3.0 Learning to predict the next word.mp4 15.0 MB
- videos/Neural Networks for Machine Learning 3.4 Ways to deal with the large number of possible outputs.mp4 14.9 MB
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- 2 - 5 - What perceptrons cant do [15 min].mp4 17.4 MB
- 8 - 2 - Modeling character strings with multiplicative connections [14 mins].mp4 17.4 MB
- 8 - 1 - A brief overview of Hessian Free optimization.mp4 17.0 MB
- 10 - 1 - Why it helps to combine models [13 min].mp4 15.9 MB
- 6 - 5 - Rmsprop Divide the gradient by a running average of its recent magnitude.mp4 15.9 MB
- 1 - 1 - Why do we need machine learning [13 min].mp4 15.8 MB
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- 6 - 2 - A bag of tricks for mini-batch gradient descent.mp4 15.6 MB
- 4 - 1 - Learning to predict the next word [13 min].mp4 15.0 MB
- 4 - 5 - Ways to deal with the large number of possible outputs [15 min].mp4 14.9 MB
- 8 - 3 - Learning to predict the next character using HF [12 mins].mp4 14.6 MB
- 9 - 1 - Overview of ways to improve generalization [12 min].mp4 14.2 MB
- 3 - 1 - Learning the weights of a linear neuron [12 min].mp4 14.2 MB
- 3 - 4 - The backpropagation algorithm [12 min].mp4 14.0 MB
- 9 - 5 - The Bayesian interpretation of weight decay [11 min].mp4 12.9 MB
- 9 - 4 - Introduction to the full Bayesian approach [12 min].mp4 12.6 MB
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