# m2dsupsdlclass/lectures-labs

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

repo name | m2dsupsdlclass/lectures-labs |

repo link | https://github.com/m2dsupsdlclass/lectures-labs |

homepage | |

language | Jupyter Notebook |

size (curr.) | 152437 kB |

stars (curr.) | 1071 |

created | 2017-02-21 |

license | MIT License |

# 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.

### 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 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

- 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

#### 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

#### 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

## 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.