Neural Networks for Machine Learning —Geoffrey Hinton
A video collection for Neural Networks for Machine Learning —Geoffrey Hinton
![Lecture 16.4 — The fog of progress — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/FOqMeBM3EIE/hqdefault.jpg)
![Lecture 16.3 — Bayesian optimization of hyper parameters — [ Deep Learning | Hinton | UofT ]](https://i.ytimg.com/vi/i0cKa0di_lo/hqdefault.jpg)
![Lecture 16.2 — Hierarchical Coordinate Frames — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/hh0tKskwyzg/hqdefault.jpg)
![Lecture 16.1 — Learning a joint model of images and captions — [ Deep Learning | Hinton | UofT ]](https://i.ytimg.com/vi/kVuF-9BaDLs/hqdefault.jpg)
![Lecture 15.6 — Shallow autoencoders for pre training — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/xjlvVfEbhz4/hqdefault.jpg)
![Lecture 15.5 — Learning binary codes for image retrieval — [ Deep Learning | Hinton | UofT ]](https://i.ytimg.com/vi/j1ry6Pg7X14/hqdefault.jpg)
![Lecture 15.4 — Semantic Hashing — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/3BDc0H9C9dw/hqdefault.jpg)
![Lecture 15.3 — Deep autoencoders for document retrieval — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/ZCNbjpcX0yg/hqdefault.jpg)
![Lecture 15.2 — Deep autoencoders — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/6jhhIPdgkp0/hqdefault.jpg)
![Lecture 15.1 — From PCA to autoencoders — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/PSOt7u8u23w/hqdefault.jpg)
![Lecture 14.5 — RBMs are infinite sigmoid belief nets — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/lgApksxm6VE/hqdefault.jpg)
![Lecture 14.4 — Modeling real valued data with an RBM — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/SnbfQwJLNk8/hqdefault.jpg)
![Lecture 14.3 — Discriminative fine tuning — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/YPQjud6JaSE/hqdefault.jpg)
![Lecture 14.2 — Discriminative learning for DBNs — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/QCBkbDpsheQ/hqdefault.jpg)
![Lecture 14.1 — Learning layers of features by stacking RBMs — [ Deep Learning | Hinton | UofT ]](https://i.ytimg.com/vi/Y3beRvYSA90/hqdefault.jpg)
![Lecture 13.4 — The wake sleep algorithm — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/FBkhbqrFyo4/hqdefault.jpg)
![Lecture 13.3 — Learning sigmoid belief nets — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/HacQtntlLcw/hqdefault.jpg)
![Lecture 13.2 — Belief Nets — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/1CgojqlHrcE/hqdefault.jpg)
![Lecture 13.1 — The ups and downs of backpropagation — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/lDFY8vQe6-g/hqdefault.jpg)
![Lecture 12.5 — RBMs for collaborative filtering — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/on5lto0rG48/hqdefault.jpg)
![Lecture 12.4 — An example of RBM learning — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/iHaS6O1eox4/hqdefault.jpg)
![Lecture 12.3 — Restricted Boltzmann Machines — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/EZOpZzUKl48/hqdefault.jpg)
![Lecture 12.2 — More efficient ways to get the statistics — [ Deep Learning | Hinton | UofT ]](https://i.ytimg.com/vi/CkZ9HA6KUnA/hqdefault.jpg)
![Lecture 12.1 — Boltzmann machine learning — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/2k9XTr_jNfE/hqdefault.jpg)
![Lecture 11.5 — How a Boltzmann machine models data — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/kytxEr0KK7Q/hqdefault.jpg)
![Lecture 11.4 — Using stochastic units to improve search — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/4vBqFO9bPeg/hqdefault.jpg)
![Lecture 11.3 — Hopfield nets with hidden units— [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/GZTmqMSxAR4/hqdefault.jpg)
![Lecture 11.2 — Dealing with spurious minima — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/HJfhdksIqUE/hqdefault.jpg)
![Lecture 11.1 — Hopfield Nets — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/Rs1XMS8NqB4/hqdefault.jpg)
![Lecture 10.5 — Dropout — [ Deep Learning | Geoffrey Hinton | Toronto ]](https://i.ytimg.com/vi/iCbVPfk_5CQ/hqdefault.jpg)
![Lecture 10.4 — Making full Bayesian learning practical — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/RsC9xfHYYH0/hqdefault.jpg)
![Lecture 10.3 — The idea of full Bayesian learning — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/1A6Md5ZYyW0/hqdefault.jpg)
![Lecture 10.2 — Mixtures of Experts — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/FxrTtRvYQWk/hqdefault.jpg)
![Lecture 10.1 — Why it helps to combine models — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/kZ7JJOMt5Kw/hqdefault.jpg)
![Lecture 9.6 — MacKay s quick and dirty method — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/CzrAOBC8ts0/hqdefault.jpg)
![Lecture 9.5 — The Bayesian interpretation of weight decay — [ Deep Learning | Hinton | UofT ]](https://i.ytimg.com/vi/PD2DmW9E_Q0/hqdefault.jpg)
![Lecture 9.4 — Introduction to the full Bayesian approach — [ Deep Learning | Hinton | UofT ]](https://i.ytimg.com/vi/jN5uYO9qllc/hqdefault.jpg)
![Lecture 9.3 — Using noise as a regularizer — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/pu2d_AFuwdQ/hqdefault.jpg)
![Lecture 9.2 — Limiting the size of the weights — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/Ukb5yqeF1po/hqdefault.jpg)
![Lecture 9.1 — Overview of ways to improve generalization — [ Deep Learning | Hinton | UofT ]](https://i.ytimg.com/vi/MbpaeKYMXVk/hqdefault.jpg)
![Lecture 8.4 — Echo State Networks — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/prXjoD9rEHo/hqdefault.jpg)
![Lecture 8.3 — Predicting the next character using HF — [ Deep Learning | Hinton | UofT ]](https://i.ytimg.com/vi/74Hj4By5kjg/hqdefault.jpg)
![Lecture 8.2 — Modeling character strings — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/GhGBrxStXuA/hqdefault.jpg)
![Lecture 8.1 — A brief overview of Hessian free optimization — [ Deep Learning | Hinton | UofT ]](https://i.ytimg.com/vi/XxQ4hgcKDlY/hqdefault.jpg)
![Lecture 7.5 — Long term Short term memory — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/OtFAECd_IXQ/hqdefault.jpg)
![Lecture 7.4 — Why it is difficult to train an RNN — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/WCngMib2mb4/hqdefault.jpg)
![Lecture 7.3 — A toy example of training an RNN — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/sGXOyI5I_Fw/hqdefault.jpg)
![Lecture 7.2 — Training RNNs with back propagation — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/4gOdNtVNZtk/hqdefault.jpg)
![Lecture 7.1 — Modeling sequences a brief overview — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/V0-2pV8vQ84/hqdefault.jpg)
![Lecture 6 5 — Rmsprop normalize the gradient — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/XhZahXzEuNo/hqdefault.jpg)
![Lecture 6.4 — Adaptive learning rates for each connection — [ Deep Learning | Hinton | UofT ]](https://i.ytimg.com/vi/Gkdq3dHYQtk/hqdefault.jpg)
![Lecture 6.3 — The momentum method Neural — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/z2rTn8Evav8/hqdefault.jpg)
![Lecture 6.2 — A bag of tricks for mini batch gradient descent — [ Deep Learning | Hinton | UofT ]](https://i.ytimg.com/vi/iNucJB-0vYs/hqdefault.jpg)
![Lecture 6.1 — Overview of mini batch gradient descent — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/EANflep-cog/hqdefault.jpg)
![Lecture 5.4 — Convolutional nets for object recognition — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/924oc7aLH-g/hqdefault.jpg)
![Lecture 5.3 — Convolutional nets for digit recognition — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/w7qvEv1EKZ0/hqdefault.jpg)
![Lecture 5.2 — Achieving viewpoint invariance — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/ARzsyifxGEI/hqdefault.jpg)
![Lecture 5.1 — Why object recognition is difficult — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/XSmoVWhM8G4/hqdefault.jpg)
![Lecture 4.5 — Dealing with many possible outputs — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/qgjzwJwKiBc/hqdefault.jpg)
![Lecture 4.4 — Neuro probabilistic language models — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/wqUgMFMnlzI/hqdefault.jpg)
![Lecture 4.3 — The softmax output function — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/PHP8beSz5o4/hqdefault.jpg)
![Lecture 4.2 — A brief diversion into cognitive science — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/QJw5CN_0zxU/hqdefault.jpg)
![Lecture 4.1 — Learning to predict the next word — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/_LzxJ1LbSl4/hqdefault.jpg)
![Lecture 3.5 — Using the derivatives from backpropagation — [ Deep Learning | Hinton | UofT ]](https://i.ytimg.com/vi/7UknKXkcbZA/hqdefault.jpg)
![Lecture 3.4 — The backpropagation algorithm — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/VCT1N0EsGj0/hqdefault.jpg)
![Lecture 3.3 — Learning weights of logistic output neuron — [ Deep Learning | Hinton | UofT ]](https://i.ytimg.com/vi/MVT-bHFqZ9o/hqdefault.jpg)
![Lecture 3.2 — The error surface for a linear neuron — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/tafPPLVuB2s/hqdefault.jpg)
![Lecture 3.1 — Learning the weights of a linear neuron — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/6gZjRI_gnGc/hqdefault.jpg)
![Lecture 2.5 — What perceptrons cant do — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/16qJwiH-FdE/hqdefault.jpg)
![Lecture 2.4 — Why the learning works — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/92hto4KwlGI/hqdefault.jpg)
![Lecture 2.3 — A geometrical view of perceptrons — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/TNhgCkYDc8M/hqdefault.jpg)
![Lecture 2.2 — Perceptrons first generation neural networks — [ Deep Learning | Hinton | UofT ]](https://i.ytimg.com/vi/5tHN6Y70d5Y/hqdefault.jpg)
![Lecture 2.1 — Types of neural network architectures — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/6cHupvcxA38/hqdefault.jpg)
![Lecture 1.5 — Three types of learning — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/nrkpEx7tA2Y/hqdefault.jpg)
![Lecture 1.4 — A simple example of learning — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/mnTJezQOIDU/hqdefault.jpg)
![Lecture 1.3 — Some simple models of neurons — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/VA9niXgGOsQ/hqdefault.jpg)
![Lecture 1.2 — What are neural networks — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/jNBYZbDWyQk/hqdefault.jpg)
![Lecture 1.1 — Why do we need machine learning — [ Deep Learning | Geoffrey Hinton | UofT ]](https://i.ytimg.com/vi/OVwEeSsSCHE/hqdefault.jpg)
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