WEBRip
English
MP4
1152 x 720
AVC ~757 kbps
30 fps
AAC
192 Kbps
44.1 KHz
2 channels
16:31:24
Genre: eLearning Video / Computer Science, Development, Programming

Welcome to my online course on neural networks! I've put this course together while teaching an in-class version of it at the Université de Sherbrooke.

This is a graduate-level course, which covers basic neural networks as well as more advanced topics, including:

Deep learning.
Conditional random fields.
Restricted Boltzmann machines.
Autoencoders.
Sparse coding.
Convolutional networks.
Vector word representations.
and many more…

In the Content section, you'll find links to video clips describing these different concepts, as well as recommended readings. The content is laid out into sections that should correspond to about one week's worth of work.

In the Evaluations section, you'll find 3 programming assignments, in Python, that I use in my class. They are good opportunities to put in practice some of the concepts covered by the course.

Content:
1 - Introduction and math revision
2 - Feedforward neural network
3 - Training neural networks
4 - Conditional random fields
5 - Training CRFs
6 - Restricted Boltzmann machine
7 - Autoencoders
8 - Deep learning
9 - Sparse coding
10 - Computer vision
11 - Natural language processing

Download File Size:5.36 GB

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