# Introduction to Deep Learning, ESPCI

This course is an introduction to deep-learning approach with lab sessions in pytorch (python module). The goal is to understand the data-driven approach and to be able to efficiently experiment with deep-learning on real data.

## News

: see the drive, for the notebooks.**Homework assignement 2**- First follow the notebook called
*idl-pytorch-starter.ipynb* - Then the assignment is also a notebook :
*hw-2-idl-espci.ipynb*

- First follow the notebook called
, before the lab session.**The deadline is the 3rd of February morning**- Next lab session: the 3rd of February, pytorch

## The resources / drive

Look at this drive for the slides and the material of lab sessions.

## Expected schedules

It starts in january 2020 (the 6th). The course are scheduled on monday, starting at 8:30 in the morning.

The first part:

- 6/01, course: introduction and basics on machine learning
- The main tasks
- Objective function
- Optimisation with gradient descent

- 13/01,
: machine learning and python, first steps**lab session**- numpy and matplotlib
- logistic regression by hand

- 20/01, course on feed-forward neural networks
- Multi-class classification
- From linear to non-linear classification
- The feed-forward architecture and the back-propagation algorithm

- 27/01, course on deep-learning
- Deep networks
- Drop out, regularization and other tricks
- pytorch overview

- 03/02,
: deep-learning in pytorch**lab session** - 10/02, course: A first review, good practices in deep-learning, problem design

The second part:

- 02/03, course: Convolutional and recurrent deep-networks
- 09/03,
: Image and sequence processing**lab session** - 23/03, course: Advanced architecture - part 1
- 30/03, course: Advanced architecture - part 2