Coursebooks

Deep learning

EE-559

Lecturer(s) :

Fleuret François

Language:

English

Summary

The objective of this course is to provide a complete introduction to deep machine learning. How to design a neural network, how to train it, and what are the modern techniques that specifically handle very large networks.

Content

The course aims at providing an overview of existing processings and methods, at teaching how to design and train a deep neural network for a given task, and at providing the theoretical basis to go beyond the topics directly seen in the course.

 

It will touch on the following topics:

 

Concepts will be illustrated with examples in the PyTorch framework (http://pytorch.org).

Keywords

machine learning, neural networks, deep learning, computer vision, python, pytorch

Learning Prerequisites

Required courses

Recommended courses

Teaching methods

Ex-cathedra with exercise sessions and mini-projects. Possibly invited speakers.

Assessment methods

Mini-projects by groups of students, and one final written exam.

 

Resources

Notes/Handbook

Not mandatory: http://www.deeplearningbook.org/

Websites

In the programs

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     
Under construction
 
      Lecture
      Exercise, TP
      Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German