Neural Networks for Machine Learning (Coursera)

by Deep Learning

Videos

6:44
Lecture 10-4. Making full Bayesian learning practical Deep Learning
8:35
Lecture 10-5. Dropout Deep Learning
13:01
Lecture 11-1. Hopfield Nets Deep Learning
11:02
Lecture 11-2. Dealing with spurious minima Deep Learning
9:39
Lecture 11-3. Hopfield nets with hidden units Deep Learning
10:24
Lecture 11-4. Using stochastic units to improv search Deep Learning
11:44
Lecture 11-5. How a Boltzmann machine models data Deep Learning
12:15
Lecture 12-1. Boltzmann machine learning Deep Learning
14:48
Lecture 12-2. (Optional) More efficient ways to get the statistics Deep Learning
10:54
Lecture 12-3. Restricted Boltzmann Machines Deep Learning
7:14
Lecture 12-4. An example of RBM learning Deep Learning
8:16
Lecture 12-5. RBMs for collaborative filtering Deep Learning
9:53
Lecture 13-1. The ups and downs of back propagation Deep Learning
12:35
Lecture 13-2. Belief Nets Deep Learning
11:25
Lecture 13-3. Learning sigmoid belief nets Deep Learning
13:14
Lecture 13-4. The wake-sleep algorithm Deep Learning
17:34
Lecture 14-1. Learning layers of features by stacking RBMs Deep Learning
9:40
Lecture 14-2. Discriminative learning for DBNs Deep Learning
8:39
Lecture 14-3. What happens during discriminative fine-tuning? Deep Learning
9:55
Lecture 14-4. Modeling real-valued data with an RBM Deep Learning
17:11
Lecture 14-5. (Optional) RBMs are infinite sigmoid belief nets Deep Learning
7:57
Lecture 15-1. From PCA to autoencoders Deep Learning
4:10
Lecture 15-2. Deep auto encoders Deep Learning
8:19
Lecture 15-3. Deep auto encoders for document retrieval Deep Learning
8:50
Lecture 15-4. Semantic Hashing Deep Learning
9:37
Lecture 15-5. Learning binary codes for image retrieval Deep Learning
7:02
Lecture 15-6. Shallow autoencoders for pre-training Deep Learning
9:04
Lecture 16-1. (Optional) Learning a joint model of images and captions Deep Learning
9:40
Lecture 16-2. (Optional) Hierarchical Coordinate Frames Deep Learning
13:29
Lecture 16-3. (Optional) Bayesian optimization of hyper-parameters Deep Learning
2:24
Lecture 16-4. (Optional) The fog of progress Deep Learning

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