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hinton_videos_slides.torrent
27.77KB
slides/lec16.pptx
336.23KB
slides/lec13.pptx
414.79KB
slides/lec10.pptx
880.45KB
videos/Neural Networks for Machine Learning 15.3 OPTIONAL The fog of progress.mp4
2.78MB
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videos/Neural Networks for Machine Learning 2.2 Learning the weights of a logistic output neuron.mp4
4.37MB
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videos/Neural Networks for Machine Learning 14.1 Deep auto encoders.mp4
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videos/Neural Networks for Machine Learning 3.1 A brief diversion into cognitive science.mp4
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videos/Neural Networks for Machine Learning 4.0 Why object recognition is difficult.mp4
5.37MB
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videos/Neural Networks for Machine Learning 2.1 The error surface for a linear neuron.mp4
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videos/Neural Networks for Machine Learning 1.3 Why the learning works.mp4
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videos/Neural Networks for Machine Learning 0.3 A simple example of learning.mp4
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videos/Neural Networks for Machine Learning 5.3 Adaptive learning rates for each connection.mp4
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videos/Neural Networks for Machine Learning 4.1 Achieving viewpoint invariance.mp4
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videos/Neural Networks for Machine Learning 6.2 A toy example of training an RNN.mp4
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videos/Neural Networks for Machine Learning 1.2 A geometrical view of perceptrons.mp4
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videos/Neural Networks for Machine Learning 6.1 Training RNNs with back propagation.mp4
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videos/Neural Networks for Machine Learning 8.1 Limiting the size of the weights.mp4
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videos/Neural Networks for Machine Learning 3.2 Another diversion The softmax output function.mp4
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videos/Neural Networks for Machine Learning 9.3 Making full Bayesian learning practical.mp4
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videos/Neural Networks for Machine Learning 14.5 Shallow autoencoders for pre-training.mp4
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videos/Neural Networks for Machine Learning 9.2 The idea of full Bayesian learning.mp4
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videos/Neural Networks for Machine Learning 8.2 Using noise as a regularizer.mp4
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videos/Neural Networks for Machine Learning 11.3 An example of RBM learning.mp4
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videos/Neural Networks for Machine Learning 1.0 Types of neural network architectures.mp4
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videos/Neural Networks for Machine Learning 6.3 Why it is difficult to train an RNN.mp4
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videos/Neural Networks for Machine Learning 3.3 Neuro-probabilistic language models.mp4
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videos/Neural Networks for Machine Learning 0.4 Three types of learning.mp4
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videos/Neural Networks for Machine Learning 0.2 Some simple models of neurons.mp4
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videos/Neural Networks for Machine Learning 1.1 Perceptrons The first generation of neural networks.mp4
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videos/Neural Networks for Machine Learning 11.4 RBMs for collaborative filtering.mp4
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videos/Neural Networks for Machine Learning 5.0 Overview of mini-batch gradient descent.mp4
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videos/Neural Networks for Machine Learning 14.0 From PCA to autoencoders.mp4
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videos/Neural Networks for Machine Learning 9.4 Dropout.mp4
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videos/Neural Networks for Machine Learning 5.2 The momentum method.mp4
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videos/Neural Networks for Machine Learning 0.1 What are neural networks.mp4
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videos/Neural Networks for Machine Learning 14.3 Semantic Hashing.mp4
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videos/Neural Networks for Machine Learning 13.2 What happens during discriminative fine-tuning.mp4
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videos/Neural Networks for Machine Learning 6.4 Long-term Short-term-memory.mp4
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videos/Neural Networks for Machine Learning 14.2 Deep auto encoders for document retrieval.mp4
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videos/Neural Networks for Machine Learning 2.4 Using the derivatives computed by backpropagation.mp4
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videos/Neural Networks for Machine Learning 15.1 OPTIONAL Hierarchical Coordinate Frames.mp4
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videos/Neural Networks for Machine Learning 13.3 Modeling real-valued data with an RBM.mp4
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videos/Neural Networks for Machine Learning 7.3 Echo State Networks.mp4
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videos/Neural Networks for Machine Learning 13.1 Discriminative learning for DBNs.mp4
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videos/Neural Networks for Machine Learning 10.2 Hopfield nets with hidden units.mp4
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videos/Neural Networks for Machine Learning 14.4 Learning binary codes for image retrieval.mp4
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videos/Neural Networks for Machine Learning 10.3 Using stochastic units to improv search.mp4
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videos/Neural Networks for Machine Learning 12.0 The ups and downs of back propagation.mp4
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videos/Neural Networks for Machine Learning 8.3 Introduction to the full Bayesian approach.mp4
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videos/Neural Networks for Machine Learning 8.4 The Bayesian interpretation of weight decay.mp4
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videos/Neural Networks for Machine Learning 11.2 Restricted Boltzmann Machines.mp4
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videos/Neural Networks for Machine Learning 10.1 Dealing with spurious minima.mp4
12.77MB
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videos/Neural Networks for Machine Learning 10.4 How a Boltzmann machine models data.mp4
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videos/Neural Networks for Machine Learning 2.3 The backpropagation algorithm.mp4
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videos/Neural Networks for Machine Learning 2.0 Learning the weights of a linear neuron.mp4
13.52MB
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videos/Neural Networks for Machine Learning 8.0 Overview of ways to improve generalization.mp4
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videos/Neural Networks for Machine Learning 12.2 Learning sigmoid belief nets.mp4
13.59MB
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videos/Neural Networks for Machine Learning 15.0 OPTIONAL Learning a joint model of images and captions.mp4
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videos/Neural Networks for Machine Learning 7.2 Learning to predict the next character using HF.mp4
13.92MB
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videos/Neural Networks for Machine Learning 11.0 Boltzmann machine learning.mp4
14.03MB
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videos/Neural Networks for Machine Learning 3.4 Ways to deal with the large number of possible outputs.mp4
14.26MB
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videos/Neural Networks for Machine Learning 3.0 Learning to predict the next word.mp4
14.28MB
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videos/Neural Networks for Machine Learning 10.0 Hopfield Nets.mp4
14.65MB
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videos/Neural Networks for Machine Learning 12.1 Belief Nets.mp4
14.86MB
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videos/Neural Networks for Machine Learning 5.1 A bag of tricks for mini-batch gradient descent.mp4
14.9MB
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videos/Neural Networks for Machine Learning 9.1 Mixtures of Experts.mp4
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videos/Neural Networks for Machine Learning 0.0 Why do we need machine learning.mp4
15.05MB
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videos/Neural Networks for Machine Learning 5.4 Rmsprop Divide the gradient by a running average of its recent magnitude.mp4
15.12MB
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videos/Neural Networks for Machine Learning 9.0 Why it helps to combine models.mp4
15.12MB
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videos/Neural Networks for Machine Learning 12.3 The wake-sleep algorithm.mp4
15.68MB
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videos/Neural Networks for Machine Learning 15.2 OPTIONAL Bayesian optimization of hyper-parameters.mp4
15.8MB
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videos/Neural Networks for Machine Learning 7.0 A brief overview of Hessian Free optimization.mp4
16.24MB
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videos/Neural Networks for Machine Learning 7.1 Modeling character strings with multiplicative connections.mp4
16.56MB
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videos/Neural Networks for Machine Learning 1.4 What perceptrons can't do.mp4
16.57MB
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videos/Neural Networks for Machine Learning 11.1 OPTIONAL VIDEO More efficient ways to get the statistics.mp4
16.93MB
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videos/Neural Networks for Machine Learning 4.2 Convolutional nets for digit recognition.mp4
18.46MB
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videos/Neural Networks for Machine Learning 13.4 OPTIONAL VIDEO RBMs are infinite sigmoid belief nets.mp4
19.44MB
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videos/Neural Networks for Machine Learning 13.0 Learning layers of features by stacking RBMs.mp4
20.07MB
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videos/Neural Networks for Machine Learning 6.0 Modeling sequences A brief overview.mp4
20.13MB
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videos/Neural Networks for Machine Learning 4.3 Convolutional nets for object recognition.mp4
23.03MB
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