Neural Networks

author: Hugo Larochelle, Twitter, Inc.
published: Aug. 23, 2016,   recorded: August 2016,   views: 32260


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In this lecture, I will cover the basic concepts behind feedforward neural networks. The talk will be split into 2 parts. In the first part, I'll cover forward propagation and backpropagation in neural networks. Specifically, I'll discuss the parameterization of feedforward nets, the most common types of units, the capacity of neural networks and how to compute the gradients of the training loss for classification with neural networks. In the second part, I'll discuss the final components necessary to train neural networks by gradient descent and then discuss the more recent ideas that are now commonly used for training deep neural networks. I will thus present different variants of gradient descent algorithms, dropout, batch normalization and unsupervised pretraining.

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Reviews and comments:

Comment1 Vinay Kumar, January 19, 2017 at 7:45 a.m.:

Is it possible to download these videos?

Comment2 Sujung Bae, May 10, 2017 at 8:25 a.m.:

Page 38 in pdf, some contents are missing. Could you upload pdf including the missing contents?

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