Hinton

/videos/Neural Networks for Machine Learning 2.4 Using the derivatives computed by backpropagation.mp4

/videos/Neural Networks for Machine Learning 12.0 The ups and downs of back propagation.mp4

/videos/Neural Networks for Machine Learning 6.1 Training RNNs with back propagation.mp4

/videos/Neural Networks for Machine Learning 2.0 Learning the weights of a linear neuron.mp4

/videos/Neural Networks for Machine Learning 5.3 Adaptive learning rates for each connection.mp4

/videos/Neural Networks for Machine Learning 2.2 Learning the weights of a logistic output neuron.mp4

/videos/Neural Networks for Machine Learning 3.2 Another diversion The softmax output function.mp4

/videos/Neural Networks for Machine Learning 8.2 Using noise as a regularizer.mp4

/videos/Neural Networks for Machine Learning 10.3 Using stochastic units to improv search.mp4

/videos/Neural Networks for Machine Learning 1.2 A geometrical view of perceptrons.mp4

/videos/Neural Networks for Machine Learning 8.1 Limiting the size of the weights.mp4

/videos/Neural Networks for Machine Learning 2.1 The error surface for a linear neuron.mp4

/videos/Neural Networks for Machine Learning 0.3 A simple example of learning.mp4

/videos/Neural Networks for Machine Learning 0.4 Three types of learning.mp4

/videos/Neural Networks for Machine Learning 8.0 Overview of ways to improve generalization.mp4

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