14:35

Lecture 2.5 — What perceptrons can't do [Neural Networks for Machine Learning]

05:39

Lecture 1.4 — A simple example of learning [Neural Networks for Machine Learning]

08:24

Lecture 1.3 — Some simple models of neurons [Neural Networks for Machine Learning]

08:31

Lecture 1.2 — What are neural networks [Neural Networks for Machine Learning]

13:15

Lecture 1.1 — Why do we need machine learning [Neural Networks for Machine Learning]

07:29

Lecture 2.1 — Types of neural network architectures [Neural Networks for Machine Learning]

07:38

Lecture 1.5 — Three types of learning [Neural Networks for Machine Learning]

08:17

Lecture 2.2 — Perceptrons: first-generation neural networks [Neural Networks for Machine Learning]

06:25

Lecture 2.3 — A geometrical view of perceptrons [Neural Networks for Machine Learning]

05:04

Lecture 3.2 — The error surface for a linear neuron [Neural Networks for Machine Learning]

11:56

Lecture 3.1 — Learning the weights of a linear neuron [Neural Networks for Machine Learning]

05:10

Lecture 2.4 — Why the learning works [Neural Networks for Machine Learning]

03:57

Lecture 3.3 — Learning weights of logistic output neuron [Neural Networks for Machine Learning]

11:52

Lecture 3.4 — The backpropagation algorithm [Neural Networks for Machine Learning]

09:50

Lecture 3.5 — Using the derivatives from backpropagation [Neural Networks for Machine Learning]

12:34

Lecture 4.1 — Learning to predict the next word [Neural Networks for Machine Learning]

04:27

Lecture 4.2 — A brief diversion into cognitive science [Neural Networks for Machine Learning]

07:21

Lecture 4.3 — The softmax output function [Neural Networks for Machine Learning]

07:53

Lecture 4.4 — Neuro-probabilistic language models [Neural Networks for Machine Learning]

12:17

Lecture 4.5 — Dealing with many possible outputs [Neural Networks for Machine Learning]

04:41

Lecture 5.1 — Why object recognition is difficult [Neural Networks for Machine Learning]

05:59

Lecture 5.2 — Achieving viewpoint invariance [Neural Networks for Machine Learning]

16:02

Lecture 5.3 — Convolutional nets for digit recognition [Neural Networks for Machine Learning]

08:23

Lecture 6.1 — Overview of mini batch gradient descent [Neural Networks for Machine Learning]

17:45

Lecture 5.4 — Convolutional nets for object recognition [Neural Networks for Machine Learning]

11:39

Lecture 6.5 — Rmsprop: normalize the gradient [Neural Networks for Machine Learning]

13:16

Lecture 6.2 — A bag of tricks for mini batch gradient descent [Neural Networks for Machine Learning]

06:24

Lecture 7.2 — Training RNNs with back propagation [Neural Networks for Machine Learning]

17:24

Lecture 7.1 — Modeling sequences: a brief overview [Neural Networks for Machine Learning]

06:15

Lecture 7.3 — A toy example of training an RNN [Neural Networks for Machine Learning]

14:36

Lecture 8.2 — Modeling character strings [Neural Networks for Machine Learning]

12:25

Lecture 8.3 — Predicting the next character using HF [Neural Networks for Machine Learning]

09:38

Lecture 8.4 — Echo State Networks [Neural Networks for Machine Learning]

14:25

Lecture 8.1 — A brief overview of Hessian-free optimization [Neural Networks for Machine Learning]

11:45

Lecture 9.1 — Overview of ways to improve generalization [Neural Networks for Machine Learning]

06:23

Lecture 9.2 — Limiting the size of the weights [Neural Networks for Machine Learning]

07:32

Lecture 9.3 — Using noise as a regularizer [Neural Networks for Machine Learning]

10:50

Lecture 9.4 — Introduction to the full Bayesian approach [Neural Networks for Machine Learning]

03:32

Lecture 9.6 — MacKay 's quick and dirty method [Neural Networks for Machine Learning]

10:53

Lecture 9.5 — The Bayesian interpretation of weight decay [Neural Networks for Machine Learning]

07:28

Lecture 10.3 — The idea of full Bayesian learning [Neural Networks for Machine Learning]

08:36

Lecture 10.5 — Dropout [Neural Networks for Machine Learning]

06:45

Lecture 10.4 — Making full Bayesian learning practical [Neural Networks for Machine Learning]

13:16

Lecture 10.2 — Mixtures of Experts [Neural Networks for Machine Learning]

13:02

Lecture 11.1 — Hopfield Nets [Neural Networks for Machine Learning]

09:40

Lecture 11.3 — Hopfield nets with hidden units [Neural Networks for Machine Learning]

11:03

Lecture 11.2 — Dealing with spurious minima [Neural Networks for Machine Learning]

13:11

10.1 — Why it helps to combine models [Neural Networks for Machine Learning]

10:25

Lecture 11.4 — Using stochastic units to improve search [Neural Networks for Machine Learning]

11:45

Lecture 11.5 — How a Boltzmann machine models data [Neural Networks for Machine Learning]

10:55

Lecture 12.3 — Restricted Boltzmann Machines [Neural Networks for Machine Learning]

12:16

Lecture 12.1 — Boltzmann machine learning [Neural Networks for Machine Learning]

07:15

Lecture 12.4 — An example of RBM learning [Neural Networks for Machine Learning]

14:49

Lecture 12.2 — More efficient ways to get the statistics [Neural Networks for Machine Learning]

08:17

Lecture 12.5 — RBMs for collaborative filtering [Neural Networks for Machine Learning]

09:54

Lecture 13.1 — The ups and downs of backpropagation [Neural Networks for Machine Learning]

12:36

Lecture 13.2 — Belief Nets [Neural Networks for Machine Learning]

09:41

Lecture 14.2 — Discriminative learning for DBNs [Neural Networks for Machine Learning]

11:26

Lecture 13.3 — Learning sigmoid belief nets [Neural Networks for Machine Learning]

13:15

Lecture 13.4 — The wake sleep algorithm [Neural Networks for Machine Learning]