Deep Learning course: lecture slides and lab notebooks

This course is being taught at as part of Master Year 2 Data Science IP-Paris

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 andadditional references.

Lab and Home Assignment Notebooks

The Jupyter notebooks for the labs can be found in the labs folder ofthe github repository:

git clone https://github.com/m2dsupsdlclass/lectures-labs

These notebooks only work with keras and tensorflowPlease follow the installation_instructions.mdto 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

Lab 6: Text Classification, Word Embeddings and Language Models

• Text Classification and Word Vectors
• Character Level Language Model (GPU required)
• Transformers (BERT fine-tuning): Joint Intent Classification and Slot Filling

Lab 7: Sequence to Sequence for Machine Translation

• Translation of Numeric Phrases with Seq2Seq

Lab 8: Intro to PyTorch

• Pytorch Introduction to Autograd
• Pytorch classification of Fashion MNIST
• Stochastic Optimization Landscape in Pytorch

Lab 9: Siamese Networks and Triplet loss

• Face verification using Siamese Nets
• Face verification using Triplet loss

Lab 10: Variational Auto Encoder

• VAE on Fashion MNIST

Acknowledgments

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

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

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

License

All the code in this repository is made available under the MIT licenseunless otherwise noted.

The slides are published under the terms of the CC-By 4.0license.