Hinton | D1FAD1776F5BE6788F143A7F02D2C75BE1EA0AE7

Hinton

Name	hinton	DOWNLOAD Copy Link
Total Size	946.3 MB
Total Files	94
Hash	D1FAD1776F5BE6788F143A7F02D2C75BE1EA0AE7

/slides/
lec7.pptx	228.0 KB
lec16.pptx	344.3 KB
lec2.pptx	409.2 KB
lec13.pptx	424.8 KB
lec8.pptx	568.2 KB
lec6.pptx	672.6 KB
lec11.pptx	743.8 KB
lec10.pptx	901.6 KB
lec4.pptx	1.1 MB
lec3.pptx	1.2 MB
lec14.pptx	1.3 MB
lec9.pptx	1.5 MB
lec5.pptx	1.7 MB
lec15.pptx	1.9 MB
lec12.pptx	2.0 MB
lec1.pptx	3.8 MB
/videos/
Neural Networks for Machine Learning 15.3 OPTIONAL The fog of progress.mp4	2.9 MB
Neural Networks for Machine Learning 2.2 Learning the weights of a logistic output neuron.mp4	4.6 MB
Neural Networks for Machine Learning 8.5 MacKay's quick and dirty method of setting weight costs.mp4	4.6 MB
Neural Networks for Machine Learning 14.1 Deep auto encoders.mp4	5.2 MB
Neural Networks for Machine Learning 3.1 A brief diversion into cognitive science.mp4	5.6 MB
Neural Networks for Machine Learning 4.0 Why object recognition is difficult.mp4	5.6 MB
Neural Networks for Machine Learning 2.1 The error surface for a linear neuron.mp4	6.2 MB
Neural Networks for Machine Learning 1.3 Why the learning works.mp4	6.2 MB
Neural Networks for Machine Learning 0.3 A simple example of learning.mp4	6.9 MB
Neural Networks for Machine Learning 5.3 Adaptive learning rates for each connection.mp4	7.0 MB
Neural Networks for Machine Learning 4.1 Achieving viewpoint invariance.mp4	7.2 MB
Neural Networks for Machine Learning 6.2 A toy example of training an RNN.mp4	7.6 MB
Neural Networks for Machine Learning 1.2 A geometrical view of perceptrons.mp4	7.7 MB
Neural Networks for Machine Learning 6.1 Training RNNs with back propagation.mp4	7.7 MB
Neural Networks for Machine Learning 8.1 Limiting the size of the weights.mp4	7.7 MB
Neural Networks for Machine Learning 3.2 Another diversion The softmax output function.mp4	8.4 MB
Neural Networks for Machine Learning 9.3 Making full Bayesian learning practical.mp4	8.5 MB
Neural Networks for Machine Learning 14.5 Shallow autoencoders for pre-training.mp4	8.7 MB
Neural Networks for Machine Learning 9.2 The idea of full Bayesian learning.mp4	8.8 MB
Neural Networks for Machine Learning 8.2 Using noise as a regularizer.mp4	8.9 MB
Neural Networks for Machine Learning 11.3 An example of RBM learning.mp4	9.1 MB
Neural Networks for Machine Learning 1.0 Types of neural network architectures.mp4	9.2 MB
Neural Networks for Machine Learning 6.3 Why it is difficult to train an RNN.mp4	9.3 MB
Neural Networks for Machine Learning 3.3 Neuro-probabilistic language models.mp4	9.4 MB
Neural Networks for Machine Learning 0.4 Three types of learning.mp4	9.4 MB
Neural Networks for Machine Learning 0.2 Some simple models of neurons.mp4	9.7 MB
Neural Networks for Machine Learning 1.1 Perceptrons The first generation of neural networks.mp4	9.8 MB
Neural Networks for Machine Learning 11.4 RBMs for collaborative filtering.mp4	10.0 MB
Neural Networks for Machine Learning 5.0 Overview of mini-batch gradient descent.mp4	10.1 MB
Neural Networks for Machine Learning 14.0 From PCA to autoencoders.mp4	10.2 MB
Neural Networks for Machine Learning 9.4 Dropout.mp4	10.2 MB
Neural Networks for Machine Learning 5.2 The momentum method.mp4	10.2 MB
Neural Networks for Machine Learning 0.1 What are neural networks.mp4	10.2 MB
Neural Networks for Machine Learning 14.3 Semantic Hashing.mp4	10.5 MB
Neural Networks for Machine Learning 13.2 What happens during discriminative fine-tuning.mp4	10.7 MB
Neural Networks for Machine Learning 6.4 Long-term Short-term-memory.mp4	10.7 MB
Neural Networks for Machine Learning 14.2 Deep auto encoders for document retrieval.mp4	10.7 MB
Neural Networks for Machine Learning 2.4 Using the derivatives computed by backpropagation.mp4	11.7 MB
Neural Networks for Machine Learning 15.1 OPTIONAL Hierarchical Coordinate Frames.mp4	11.7 MB
Neural Networks for Machine Learning 13.3 Modeling real-valued data with an RBM.mp4	11.7 MB
Neural Networks for Machine Learning 7.3 Echo State Networks.mp4	11.8 MB
Neural Networks for Machine Learning 13.1 Discriminative learning for DBNs.mp4	11.8 MB
Neural Networks for Machine Learning 10.2 Hopfield nets with hidden units.mp4	11.9 MB
Neural Networks for Machine Learning 14.4 Learning binary codes for image retrieval.mp4	12.1 MB
Neural Networks for Machine Learning 10.3 Using stochastic units to improv search.mp4	12.3 MB
Neural Networks for Machine Learning 12.0 The ups and downs of back propagation.mp4	12.4 MB
Neural Networks for Machine Learning 8.3 Introduction to the full Bayesian approach.mp4	12.6 MB
Neural Networks for Machine Learning 8.4 The Bayesian interpretation of weight decay.mp4	12.9 MB
Neural Networks for Machine Learning 11.2 Restricted Boltzmann Machines.mp4	13.3 MB
Neural Networks for Machine Learning 10.1 Dealing with spurious minima.mp4	13.4 MB
Neural Networks for Machine Learning 10.4 How a Boltzmann machine models data.mp4	13.9 MB
Neural Networks for Machine Learning 2.3 The backpropagation algorithm.mp4	14.0 MB
Neural Networks for Machine Learning 2.0 Learning the weights of a linear neuron.mp4	14.2 MB
Neural Networks for Machine Learning 8.0 Overview of ways to improve generalization.mp4	14.2 MB
Neural Networks for Machine Learning 12.2 Learning sigmoid belief nets.mp4	14.3 MB
Neural Networks for Machine Learning 15.0 OPTIONAL Learning a joint model of images and captions.mp4	14.5 MB
Neural Networks for Machine Learning 7.2 Learning to predict the next character using HF.mp4	14.6 MB
Neural Networks for Machine Learning 11.0 Boltzmann machine learning.mp4	14.7 MB
Neural Networks for Machine Learning 3.4 Ways to deal with the large number of possible outputs.mp4	14.9 MB
Neural Networks for Machine Learning 3.0 Learning to predict the next word.mp4	15.0 MB
Neural Networks for Machine Learning 10.0 Hopfield Nets.mp4	15.4 MB
Neural Networks for Machine Learning 12.1 Belief Nets.mp4	15.6 MB
Neural Networks for Machine Learning 5.1 A bag of tricks for mini-batch gradient descent.mp4	15.6 MB
Neural Networks for Machine Learning 9.1 Mixtures of Experts.mp4	15.7 MB
Neural Networks for Machine Learning 0.0 Why do we need machine learning.mp4	15.8 MB
Neural Networks for Machine Learning 5.4 Rmsprop Divide the gradient by a running average of its recent magnitude.mp4	15.9 MB
Neural Networks for Machine Learning 9.0 Why it helps to combine models.mp4	15.9 MB
Neural Networks for Machine Learning 12.3 The wake-sleep algorithm.mp4	16.4 MB
Neural Networks for Machine Learning 15.2 OPTIONAL Bayesian optimization of hyper-parameters.mp4	16.6 MB
Neural Networks for Machine Learning 7.0 A brief overview of Hessian Free optimization.mp4	17.0 MB
Neural Networks for Machine Learning 7.1 Modeling character strings with multiplicative connections.mp4	17.4 MB
Neural Networks for Machine Learning 1.4 What perceptrons can't do.mp4	17.4 MB
Neural Networks for Machine Learning 11.1 OPTIONAL VIDEO More efficient ways to get the statistics.mp4	17.8 MB
Neural Networks for Machine Learning 4.2 Convolutional nets for digit recognition.mp4	19.4 MB
Neural Networks for Machine Learning 13.4 OPTIONAL VIDEO RBMs are infinite sigmoid belief nets.mp4	20.4 MB
Neural Networks for Machine Learning 13.0 Learning layers of features by stacking RBMs.mp4	21.0 MB
Neural Networks for Machine Learning 6.0 Modeling sequences A brief overview.mp4	21.1 MB
Neural Networks for Machine Learning 4.3 Convolutional nets for object recognition.mp4	24.1 MB
Total files 94