Deep Learning course: lecture slides and lab notebooks
This course is being taught at as part of Master Datascience Paris Saclay
Table of contents
The course covers the basics of Deep Learning, with a focus on applications.
- 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 in rendered mode):
Lab 1: Intro to Deep Learning
Lab 2: Neural Networks and Backpropagation
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
- 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
Lab 6: Text Classification, Word Embeddings and Language Models
Lab 7: Sequence to Sequence for Machine Translation
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
Lab 10: Variational Auto Encoder
This 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.