Neural Networks for Machine Learning — Geoffrey Hinton, UofT [FULL COURSE]
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This course includes
- 12.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 78 lessons • 12.5 hours of video
Neural Networks for Machine Learning — Geoffrey Hinton, UofT [FULL COURSE]
78 lessons
• 12.5 hours
Neural Networks for Machine Learning — Geoffrey Hinton, UofT [FULL COURSE]
78 lessons
• 12.5 hours
- Lecture 1.1 — Why do we need machine learning — [ Deep Learning | Geoffrey Hinton | UofT ] 13:15
- Lecture 1.2 — What are neural networks — [ Deep Learning | Geoffrey Hinton | UofT ] 08:31
- Lecture 1.3 — Some simple models of neurons — [ Deep Learning | Geoffrey Hinton | UofT ] 08:24
- Lecture 1.4 — A simple example of learning — [ Deep Learning | Geoffrey Hinton | UofT ] 05:39
- Lecture 1.5 — Three types of learning — [ Deep Learning | Geoffrey Hinton | UofT ] 07:38
- Lecture 2.1 — Types of neural network architectures — [ Deep Learning | Geoffrey Hinton | UofT ] 07:29
- Lecture 2.2 — Perceptrons first generation neural networks — [ Deep Learning | Hinton | UofT ] 08:17
- Lecture 2.3 — A geometrical view of perceptrons — [ Deep Learning | Geoffrey Hinton | UofT ] 06:25
- Lecture 2.4 — Why the learning works — [ Deep Learning | Geoffrey Hinton | UofT ] 05:10
- Lecture 2.5 — What perceptrons cant do — [ Deep Learning | Geoffrey Hinton | UofT ] 14:35
- Lecture 3.1 — Learning the weights of a linear neuron — [ Deep Learning | Geoffrey Hinton | UofT ] 11:56
- Lecture 3.2 — The error surface for a linear neuron — [ Deep Learning | Geoffrey Hinton | UofT ] 05:04
- Lecture 3.3 — Learning weights of logistic output neuron — [ Deep Learning | Hinton | UofT ] 03:57
- Lecture 3.4 — The backpropagation algorithm — [ Deep Learning | Geoffrey Hinton | UofT ] 11:52
- Lecture 3.5 — Using the derivatives from backpropagation — [ Deep Learning | Hinton | UofT ] 09:50
- Lecture 4.1 — Learning to predict the next word — [ Deep Learning | Geoffrey Hinton | UofT ] 12:34
- Lecture 4.2 — A brief diversion into cognitive science — [ Deep Learning | Geoffrey Hinton | UofT ] 04:27
- Lecture 4.3 — The softmax output function — [ Deep Learning | Geoffrey Hinton | UofT ] 07:21
- Lecture 4.4 — Neuro probabilistic language models — [ Deep Learning | Geoffrey Hinton | UofT ] 07:53
- Lecture 4.5 — Dealing with many possible outputs — [ Deep Learning | Geoffrey Hinton | UofT ] 12:17
- Lecture 5.1 — Why object recognition is difficult — [ Deep Learning | Geoffrey Hinton | UofT ] 04:41
- Lecture 5.2 — Achieving viewpoint invariance — [ Deep Learning | Geoffrey Hinton | UofT ] 05:59
- Lecture 5.3 — Convolutional nets for digit recognition — [ Deep Learning | Geoffrey Hinton | UofT ] 16:02
- Lecture 5.4 — Convolutional nets for object recognition — [ Deep Learning | Geoffrey Hinton | UofT ] 17:45
- Lecture 6.1 — Overview of mini batch gradient descent — [ Deep Learning | Geoffrey Hinton | UofT ] 08:23
- Lecture 6.2 — A bag of tricks for mini batch gradient descent — [ Deep Learning | Hinton | UofT ] 13:16
- Lecture 6.3 — The momentum method Neural — [ Deep Learning | Geoffrey Hinton | UofT ] 08:43
- Lecture 6.4 — Adaptive learning rates for each connection — [ Deep Learning | Hinton | UofT ] 05:45
- Lecture 6 5 — Rmsprop normalize the gradient — [ Deep Learning | Geoffrey Hinton | UofT ] 11:39
- Lecture 7.1 — Modeling sequences a brief overview — [ Deep Learning | Geoffrey Hinton | UofT ] 17:24
- Lecture 7.2 — Training RNNs with back propagation — [ Deep Learning | Geoffrey Hinton | UofT ] 06:24
- Lecture 7.3 — A toy example of training an RNN — [ Deep Learning | Geoffrey Hinton | UofT ] 06:15
- Lecture 7.4 — Why it is difficult to train an RNN — [ Deep Learning | Geoffrey Hinton | UofT ] 07:44
- Lecture 7.5 — Long term Short term memory — [ Deep Learning | Geoffrey Hinton | UofT ] 09:16
- Lecture 8.1 — A brief overview of Hessian free optimization — [ Deep Learning | Hinton | UofT ] 14:25
- Lecture 8.2 — Modeling character strings — [ Deep Learning | Geoffrey Hinton | UofT ] 14:36
- Lecture 8.3 — Predicting the next character using HF — [ Deep Learning | Hinton | UofT ] 12:25
- Lecture 8.4 — Echo State Networks — [ Deep Learning | Geoffrey Hinton | UofT ] 09:38
- Lecture 9.1 — Overview of ways to improve generalization — [ Deep Learning | Hinton | UofT ] 11:45
- Lecture 9.2 — Limiting the size of the weights — [ Deep Learning | Geoffrey Hinton | UofT ] 06:23
- Lecture 9.3 — Using noise as a regularizer — [ Deep Learning | Geoffrey Hinton | UofT ] 07:32
- Lecture 9.4 — Introduction to the full Bayesian approach — [ Deep Learning | Hinton | UofT ] 10:50
- Lecture 9.5 — The Bayesian interpretation of weight decay — [ Deep Learning | Hinton | UofT ] 10:53
- Lecture 9.6 — MacKay s quick and dirty method — [ Deep Learning | Geoffrey Hinton | UofT ] 03:32
- Lecture 10.1 — Why it helps to combine models — [ Deep Learning | Geoffrey Hinton | UofT ] 13:11
- Lecture 10.2 — Mixtures of Experts — [ Deep Learning | Geoffrey Hinton | UofT ] 13:16
- Lecture 10.3 — The idea of full Bayesian learning — [ Deep Learning | Geoffrey Hinton | UofT ] 07:28
- Lecture 10.4 — Making full Bayesian learning practical — [ Deep Learning | Geoffrey Hinton | UofT ] 06:45
- Lecture 10.5 — Dropout — [ Deep Learning | Geoffrey Hinton | Toronto ] 08:36
- Lecture 11.1 — Hopfield Nets — [ Deep Learning | Geoffrey Hinton | UofT ] 13:02
- Lecture 11.2 — Dealing with spurious minima — [ Deep Learning | Geoffrey Hinton | UofT ] 11:03
- Lecture 11.3 — Hopfield nets with hidden units— [ Deep Learning | Geoffrey Hinton | UofT ] 09:40
- Lecture 11.4 — Using stochastic units to improve search — [ Deep Learning | Geoffrey Hinton | UofT ] 10:25
- Lecture 11.5 — How a Boltzmann machine models data — [ Deep Learning | Geoffrey Hinton | UofT ] 11:45
- Lecture 12.1 — Boltzmann machine learning — [ Deep Learning | Geoffrey Hinton | UofT ] 12:16
- Lecture 12.2 — More efficient ways to get the statistics — [ Deep Learning | Hinton | UofT ] 14:49
- Lecture 12.3 — Restricted Boltzmann Machines — [ Deep Learning | Geoffrey Hinton | UofT ] 10:55
- Lecture 12.4 — An example of RBM learning — [ Deep Learning | Geoffrey Hinton | UofT ] 07:15
- Lecture 12.5 — RBMs for collaborative filtering — [ Deep Learning | Geoffrey Hinton | UofT ] 08:17
- Lecture 13.1 — The ups and downs of backpropagation — [ Deep Learning | Geoffrey Hinton | UofT ] 09:54
- Lecture 13.2 — Belief Nets — [ Deep Learning | Geoffrey Hinton | UofT ] 12:36
- Lecture 13.3 — Learning sigmoid belief nets — [ Deep Learning | Geoffrey Hinton | UofT ] 11:26
- Lecture 13.4 — The wake sleep algorithm — [ Deep Learning | Geoffrey Hinton | UofT ] 13:15
- Lecture 14.1 — Learning layers of features by stacking RBMs — [ Deep Learning | Hinton | UofT ] 17:35
- Lecture 14.2 — Discriminative learning for DBNs — [ Deep Learning | Geoffrey Hinton | UofT ] 09:41
- Lecture 14.3 — Discriminative fine tuning — [ Deep Learning | Geoffrey Hinton | UofT ] 08:40
- Lecture 14.4 — Modeling real valued data with an RBM — [ Deep Learning | Geoffrey Hinton | UofT ] 09:57
- Lecture 14.5 — RBMs are infinite sigmoid belief nets — [ Deep Learning | Geoffrey Hinton | UofT ] 17:12
- Lecture 15.1 — From PCA to autoencoders — [ Deep Learning | Geoffrey Hinton | UofT ] 07:58
- Lecture 15.2 — Deep autoencoders — [ Deep Learning | Geoffrey Hinton | UofT ] 04:11
- Lecture 15.3 — Deep autoencoders for document retrieval — [ Deep Learning | Geoffrey Hinton | UofT ] 08:20
- Lecture 15.4 — Semantic Hashing — [ Deep Learning | Geoffrey Hinton | UofT ] 08:51
- Lecture 15.5 — Learning binary codes for image retrieval — [ Deep Learning | Hinton | UofT ] 09:38
- Lecture 15.6 — Shallow autoencoders for pre training — [ Deep Learning | Geoffrey Hinton | UofT ] 07:03
- Lecture 16.1 — Learning a joint model of images and captions — [ Deep Learning | Hinton | UofT ] 09:05
- Lecture 16.2 — Hierarchical Coordinate Frames — [ Deep Learning | Geoffrey Hinton | UofT ] 09:41
- Lecture 16.3 — Bayesian optimization of hyper parameters — [ Deep Learning | Hinton | UofT ] 13:30
- Lecture 16.4 — The fog of progress — [ Deep Learning | Geoffrey Hinton | UofT ] 02:25
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