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Lecture 2.5 — What perceptrons can't do [Neural Networks for Machine Learning]
14:35
Lecture 2.5 — What perceptrons can't do [Neural Networks for Machine Learning]
Lecture 1.4 — A simple example of learning [Neural Networks for Machine Learning]
5:39
Lecture 1.4 — A simple example of learning [Neural Networks for Machine Learning]
Lecture 1.3 — Some simple models of neurons [Neural Networks for Machine Learning]
8:24
Lecture 1.3 — Some simple models of neurons [Neural Networks for Machine Learning]
Lecture 1.2 — What are neural networks [Neural Networks for Machine Learning]
8:31
Lecture 1.2 — What are neural networks [Neural Networks for Machine Learning]
Lecture 1.1 — Why do we need machine learning [Neural Networks for Machine Learning]
13:15
Lecture 1.1 — Why do we need machine learning [Neural Networks for Machine Learning]
Lecture 2.1 — Types of neural network architectures [Neural Networks for Machine Learning]
7:29
Lecture 2.1 — Types of neural network architectures [Neural Networks for Machine Learning]
Lecture 1.5 — Three types of learning [Neural Networks for Machine Learning]
7:38
Lecture 1.5 — Three types of learning [Neural Networks for Machine Learning]
Lecture 2.2 — Perceptrons: first-generation neural networks [Neural Networks for Machine Learning]
8:17
Lecture 2.2 — Perceptrons: first-generation neural networks [Neural Networks for Machine Learning]
Lecture 2.3 — A geometrical view of perceptrons [Neural Networks for Machine Learning]
6:25
Lecture 2.3 — A geometrical view of perceptrons [Neural Networks for Machine Learning]
Lecture 3.2 — The error surface for a linear neuron [Neural Networks for Machine Learning]
5:04
Lecture 3.2 — The error surface for a linear neuron [Neural Networks for Machine Learning]
Lecture 3.1 — Learning the weights of 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]
Lecture 2.4 — Why the learning works [Neural Networks for Machine Learning]
5:10
Lecture 2.4 — Why the learning works [Neural Networks for Machine Learning]
Lecture 3.3 — Learning weights of logistic output neuron [Neural Networks for Machine Learning]
3:57
Lecture 3.3 — Learning weights of logistic output neuron [Neural Networks for Machine Learning]
Lecture 3.4 — The backpropagation algorithm [Neural Networks for Machine Learning]
11:52
Lecture 3.4 — The backpropagation algorithm [Neural Networks for Machine Learning]
Lecture 3.5 — Using the derivatives from backpropagation [Neural Networks for Machine Learning]
9:50
Lecture 3.5 — Using the derivatives from backpropagation [Neural Networks for Machine Learning]
Lecture 4.1 — Learning to predict the next word [Neural Networks for Machine Learning]
12:34
Lecture 4.1 — Learning to predict the next word [Neural Networks for Machine Learning]
Lecture 4.2 — A brief diversion into cognitive science [Neural Networks for Machine Learning]
4:27
Lecture 4.2 — A brief diversion into cognitive science [Neural Networks for Machine Learning]
Lecture 4.3 — The softmax output function [Neural Networks for Machine Learning]
7:21
Lecture 4.3 — The softmax output function [Neural Networks for Machine Learning]
Lecture 4.4 — Neuro-probabilistic language models [Neural Networks for Machine Learning]
7:53
Lecture 4.4 — Neuro-probabilistic language models [Neural Networks for Machine Learning]
Lecture 4.5 — Dealing with many possible outputs [Neural Networks for Machine Learning]
12:17
Lecture 4.5 — Dealing with many possible outputs [Neural Networks for Machine Learning]
Lecture 5.1 — Why object recognition is difficult [Neural Networks for Machine Learning]
4:41
Lecture 5.1 — Why object recognition is difficult [Neural Networks for Machine Learning]
Lecture 5.2 — Achieving viewpoint invariance [Neural Networks for Machine Learning]
5:59
Lecture 5.2 — Achieving viewpoint invariance [Neural Networks for Machine Learning]
Lecture 5.3 — Convolutional nets for digit recognition [Neural Networks for Machine Learning]
16:02
Lecture 5.3 — Convolutional nets for digit recognition [Neural Networks for Machine Learning]
Lecture 6.1 — Overview of mini batch gradient descent [Neural Networks for Machine Learning]
8:23
Lecture 6.1 — Overview of mini batch gradient descent [Neural Networks for Machine Learning]
Lecture 5.4 — Convolutional nets for object recognition [Neural Networks for Machine Learning]
17:45
Lecture 5.4 — Convolutional nets for object recognition [Neural Networks for Machine Learning]
Lecture 6.5 — Rmsprop: normalize the gradient [Neural Networks for Machine Learning]
11:39
Lecture 6.5 — Rmsprop: normalize the gradient [Neural Networks for Machine Learning]
Lecture 6.2 — A bag of tricks for mini batch gradient descent [Neural Networks for Machine Learning]
13:16
Lecture 6.2 — A bag of tricks for mini batch gradient descent [Neural Networks for Machine Learning]
Lecture 7.2 — Training RNNs with back propagation [Neural Networks for Machine Learning]
6:24
Lecture 7.2 — Training RNNs with back propagation [Neural Networks for Machine Learning]
Lecture 7.1 — Modeling sequences: a brief overview [Neural Networks for Machine Learning]
17:24
Lecture 7.1 — Modeling sequences: a brief overview [Neural Networks for Machine Learning]
Lecture 7.3 — A toy example of training an RNN [Neural Networks for Machine Learning]
6:15
Lecture 7.3 — A toy example of training an RNN [Neural Networks for Machine Learning]