Deep Learning course: lecture slides and lab notebooks

This is a short adaptation of the original course though in Master Year 2 Data Science IP-Paris by Olivier Grisel and Charles Ollion

Table of contents

The course covers the basics of Deep Learning, with a focus on applications.

Lecture slides

• Intro to Deep Learning
• Neural Networks and Backpropagation
• Embeddings and Recommender Systems
• Convolutional Neural Networks for Image Classification
• Deep Learning for Object Detection and Image Segmentation

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 in rendered mode):

Lab 1: Intro to Deep Learning

• Demo: Object Detection with pretrained RetinaNet with Keras
• Intro to MLP with Keras

Lab 2: Neural Networks and Backpropagation

• Backpropagation in Neural Networks using Numpy
• Bonus: Backpropagation using TensorFlow

Lab 3: Embeddings and Recommender Systems

• Short Intro to Embeddings with Keras
• Neural Recommender Systems with Explicit Feedback
• Neural Recommender Systems with Implicit Feedback and the Triplet Loss

Lab 4: Convolutional Neural Networks for Image Classification

• Convolutions
• Pretrained ConvNets with Keras
• Fine Tuning a pretrained ConvNet with Keras (GPU required)
• Bonus: Convolution and ConvNets with TensorFlow

Lab 5: Deep Learning for Object Dection and Image Segmentation

• Fully Convolutional Neural Networks
• ConvNets for Classification and Localization

Acknowledgments

This lecture is built and maintained by Olivier Grisel and Charles Ollion

Charles Ollion, head of research at Heuritech - Olivier Grisel, software engineer at Inria

We thank the Orange-Keyrus-Thalès chair for supporting this class.

License

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.