Deep Learning course: lecture slides and lab notebooks
Table of contents
The course covers the basics of Deep Learning, with a focus on applications.
- Intro to Deep Learning (HTML | PDF)
- Neural Networks and Backpropagation (HTML | PDF)
- Convolutional Neural Networks for Image Classification (HTML | PDF)
- Embeddings and Recommender systems (HTML | PDF)
Note: press "P" to display the presenter's notes that include some comments and additional references.
Lab and Home Assignment Notebooks
The Jupyter notebooks for the labs can be found in the
labs folder of
the github repository:
git clone https://github.com/rth/dl-lectures-labs
These notebooks only work with
keras and tensorflow
Please follow the installation_instructions.md
to get started.
Direct links to the rendered notebooks including solutions (to be updated):
Lab 1: Neural Networks and Backpropagation
Lab 2: Convolutional Neural Networks for Image Classification
Lab 3: Embedding and Recommender systems
Lab 4: Natual Language Processing
The original lecture is built and maintained by Olivier Grisel and Charles Ollion
We thank the Orange-Keyrus-Thalès chair for supporting this class.
All the code in this repository is made available under the MIT license unless otherwise noted.
The slides are published under the terms of the CC-By 4.0 license.