This course is being taught at as part of Master Year 2 Data Science IP-Paris
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.
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):
- Short Intro to Embeddings with Keras
- Neural Recommender Systems with Explicit Feedback
- Neural Recommender Systems with Implicit Feedback and the Triplet Loss
- Convolutions
- Pretrained ConvNets with Keras
- Fine Tuning a pretrained ConvNet with Keras (GPU required)
- Bonus: Convolution and ConvNets with TensorFlow
- Text Classification and Word Vectors
- Character Level Language Model (GPU required)
- Transformers (BERT fine-tuning): Joint Intent Classification and Slot Filling
- Pytorch Introduction to Autograd
- Pytorch classification of Fashion MNIST
- Stochastic Optimization Landscape in Pytorch
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.
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.