cadad/lectures-labs

Slides and Jupyter notebooks for the Deep Learning lectures at M2 Data Science Université Paris Saclay

Deep Learning course: lecture slides and lab notebooks

This course is being taught at as part of Master Datascience Paris Saclay

Note: We're adding content to this lecture for early 2018, expect a few broken things in the notebooks and slides during this process

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
• Recurrent Neural Networks and NLP
• Sequence to sequence, attention and memory
• Expressivity, Optimization and Generalization
• Imbalanced classification and metric learning
• Unsupervised Deep Learning and Generative models

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/m2dsupsdlclass/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

• Intro to MLP with Keras, Numpy and TensorFlow

Lab 2: 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 3: Convolutional Neural Networks for Image Classification

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

Lab 4: Deep Learning for Object Dection and Image Segmentation

• Fully Convolutional Neural Networks
• ConvNets for Classification and Localization

Lab 5: Text Classification, Word Embeddings and Language Models

• Text Classification and Word Vectors
• Character Level Language Model (GPU required)

Lab 6: Sequence to Sequence for Machine Translation

• Translation of Numeric Phrases with Seq2Seq

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.