Neural networks class - Université de Sherbrooke

by Hugo Larochelle
92 videos
95,347 views
16 hours

These are the videos I use to teach my Neural networks class at Université de Sherbrooke. The videos, along with the slides and research paper references, are available here: http://tinyurl.com/qccl66y .

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Neural networks [1.1] : Feedforward neural network - artificial neuron	Hugo Larochelle	7:51
Neural networks [1.2] : Feedforward neural network - activation function	Hugo Larochelle	5:56
Neural networks [1.3] : Feedforward neural network - capacity of single neuron	Hugo Larochelle	8:05
Neural networks [1.4] : Feedforward neural network - multilayer neural network	Hugo Larochelle	13:11
Neural networks [1.5] : Feedforward neural network - capacity of neural network	Hugo Larochelle	8:56
Neural networks [1.6] : Feedforward neural network - biological inspiration	Hugo Larochelle	14:21
Neural networks [2.1] : Training neural networks - empirical risk minimization	Hugo Larochelle	10:28
Neural networks [2.2] : Training neural networks - loss function	Hugo Larochelle	4:49
Neural networks [2.3] : Training neural networks - output layer gradient	Hugo Larochelle	12:03
Neural networks [2.4] : Training neural networks - hidden layer gradient	Hugo Larochelle	15:15
Neural networks [2.5] : Training neural networks - activation function derivative	Hugo Larochelle	4:37
Neural networks [2.6] : Training neural networks - parameter gradient	Hugo Larochelle	6:26
Neural networks [2.7] : Training neural networks - backpropagation	Hugo Larochelle	15:06
Neural networks [2.8] : Training neural networks - regularization	Hugo Larochelle	13:15
Neural networks [2.9] : Training neural networks - parameter initialization	Hugo Larochelle	6:10
Neural networks [2.10] : Training neural networks - model selection	Hugo Larochelle	13:48
Neural networks [2.11] : Training neural networks - optimization	Hugo Larochelle	23:40
Neural networks [3.1] : Conditional random fields - motivation	Hugo Larochelle	5:19
Neural networks [3.2] : Conditional random fields - linear chain CRF	Hugo Larochelle	9:58
Neural networks [3.3] : Conditional random fields - context window	Hugo Larochelle	12:47
Neural networks [3.4] : Conditional random fields - computing the partition function	Hugo Larochelle	24:34
Neural networks [3.5] : Conditional random fields - computing marginals	Hugo Larochelle	9:08
Neural networks [3.6] : Conditional random fields - performing classification	Hugo Larochelle	18:32
Neural networks [3.7] : Conditional random fields - factors, sufficient statistics and linear CRF	Hugo Larochelle	11:37
Neural networks [3.8] : Conditional random fields - Markov network	Hugo Larochelle	11:37
Neural networks [3.9] : Conditional random fields - factor graph	Hugo Larochelle	6:28
Neural networks [3.10] : Conditional random fields - belief propagation	Hugo Larochelle	24:48
Neural networks [4.1] : Training CRFs - loss function	Hugo Larochelle	5:45
Neural networks [4.2] : Training CRFs - unary log-factor gradient	Hugo Larochelle	13:29
Neural networks [4.3] : Training CRFs - pairwise log-factor gradient	Hugo Larochelle	5:54
Neural networks [4.4] : Training CRFs - discriminative vs. generative learning	Hugo Larochelle	6:44
Neural networks [4.5] : Training CRFs - maximum-entropy Markov model	Hugo Larochelle	8:46
Neural networks [4.6] : Training CRFs - hidden Markov model	Hugo Larochelle	4:17
Neural networks [4.7] : Training CRFs - general conditional random field	Hugo Larochelle	6:30
Neural networks [4.8] : Training CRFs - pseudolikelihood	Hugo Larochelle	5:11
Neural networks [5.1] : Restricted Boltzmann machine - definition	Hugo Larochelle	12:17
Neural networks [5.2] : Restricted Boltzmann machine - inference	Hugo Larochelle	18:32
Neural networks [5.3] : Restricted Boltzmann machine - free energy	Hugo Larochelle	12:54
Neural networks [5.4] : Restricted Boltzmann machine - contrastive divergence	Hugo Larochelle	13:34
Neural networks [5.5] : Restricted Boltzmann machine - contrastive divergence (parameter update)	Hugo Larochelle	11:10
Neural networks [5.6] : Restricted Boltzmann machine - persistent CD	Hugo Larochelle	7:36
Neural networks [5.7] : Restricted Boltzmann machine - example	Hugo Larochelle	8:15
Neural networks [5.8] : Restricted Boltzmann machine - extensions	Hugo Larochelle	9:19
Neural networks [6.1] : Autoencoder - definition	Hugo Larochelle	6:15
Neural networks [6.2] : Autoencoder - loss function	Hugo Larochelle	11:52
Neural networks [6.3] : Autoencoder - example	Hugo Larochelle	2:54
Neural networks [6.4] : Autoencoder - linear autoencoder	Hugo Larochelle	19:47
Neural networks [6.5] : Autoencoder - undercomplete vs. overcomplete hidden layer	Hugo Larochelle	5:36
Neural networks [6.6] : Autoencoder - denoising autoencoder	Hugo Larochelle	14:16
Neural networks [6.7] : Autoencoder - contractive autoencoder	Hugo Larochelle	12:08
Neural networks [7.1] : Deep learning - motivation	Hugo Larochelle	15:12
Neural networks [7.2] : Deep learning - difficulty of training	Hugo Larochelle	8:24
Neural networks [7.3] : Deep learning - unsupervised pre-training	Hugo Larochelle	12:52
Neural networks [7.4] : Deep learning - example	Hugo Larochelle	12:41
Neural networks [7.5] : Deep learning - dropout	Hugo Larochelle	11:18
Neural networks [7.6] : Deep learning - deep autoencoder	Hugo Larochelle	7:34
Neural networks [7.7] : Deep learning - deep belief network	Hugo Larochelle	13:22
Neural networks [7.8] : Deep learning - variational bound	Hugo Larochelle	14:03
Neural networks [7.9] : Deep learning - DBN pre-training	Hugo Larochelle	20:00
Neural networks [8.1] : Sparse coding - definition	Hugo Larochelle	12:05
Neural networks [8.2] : Sparse coding - inference (ISTA algorithm)	Hugo Larochelle	12:36
Neural networks [8.3] : Sparse coding - dictionary update with projected gradient descent	Hugo Larochelle	5:04
Neural networks [8.4] : Sparse coding - dictionary update with block-coordinate descent	Hugo Larochelle	13:10
Neural networks [8.5] : Sparse coding - dictionary learning algorithm	Hugo Larochelle	5:31
Neural networks [8.6] : Sparse coding - online dictionary learning algorithm	Hugo Larochelle	9:05
Neural networks [8.7] : Sparse coding - ZCA preprocessing	Hugo Larochelle	8:39
Neural networks [8.8] : Sparse coding - feature extraction	Hugo Larochelle	10:43
Neural networks [8.9] : relationship with V1	Hugo Larochelle	5:46
Neural networks [9.1] : Computer vision - motivation	Hugo Larochelle	5:25
Neural networks [9.2] : Computer vision - local connectivity	Hugo Larochelle	4:19
Neural networks [9.3] : Computer vision - parameter sharing	Hugo Larochelle	11:32
Neural networks [9.4] : Computer vision - discrete convolution	Hugo Larochelle	15:27
Neural networks [9.5] : Computer vision - pooling and subsampling	Hugo Larochelle	8:11
Neural networks [9.6] : Computer vision - convolutional network	Hugo Larochelle	13:58
Neural networks [9.7] : Computer vision - object recognition	Hugo Larochelle	8:00
Neural networks [9.8] : Computer vision - example	Hugo Larochelle	14:20
Neural networks [9.9] : Computer vision - data set expansion	Hugo Larochelle	7:32
Neural networks [9.10] : Computer vision - convolutional RBM	Hugo Larochelle	10:46
Neural networks [10.1] : Natural language processing - motivation	Hugo Larochelle	2:16
Neural networks [10.2] : Natural language processing - preprocessing	Hugo Larochelle	9:46
Neural networks [10.3] : Natural language processing - one-hot encoding	Hugo Larochelle	7:31
Neural networks [10.4] : Natural language processing - word representations	Hugo Larochelle	10:30
Neural networks [10.5] : Natural language processing - language modeling	Hugo Larochelle	9:23
Neural networks [10.6] : Natural language processing - neural network language model	Hugo Larochelle	16:08
Neural networks [10.7] : Natural language processing - hierarchical output layer	Hugo Larochelle	13:51
Neural networks [10.8] : Natural language processing - word tagging	Hugo Larochelle	10:48
Neural networks [10.9] : Natural language processing - convolutional network	Hugo Larochelle	16:44
Neural networks [10.10] : Natural language processing - multitask learning	Hugo Larochelle	16:03
Neural networks [10.11] : Natural language processing - recursive network	Hugo Larochelle	5:50
Neural networks [10.12] : Natural language processing - merging representations	Hugo Larochelle	3:40
Neural networks [10.13] : Natural language processing - tree inference	Hugo Larochelle	16:51
Neural networks [10.14] : Natural language processing - recursive network training	Hugo Larochelle	13:29

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Hugo Larochelle

Neural networks class - Université de Sherbrooke

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