lsml21

Notebooks for the 2021 course on Large Scale Machine Learning at Mines ParisTech.

Day 1: Introduction to large scale ML

• If you are familiar with scikit-learn (for example, Mines students who had Data Science in their first year or took S1333-5 previously), work on notebook 1_sklearn_at_scale.ipynb.
• If you are not familiar with scikit-learn, you can start with this notebook to get a hang of things.

Day 2: Deep learning

• If you have never trained convolutional neural networks on keras, start with this notebook to train a LeNet Deep Convolutional Network on MNIST.
• Practice transfer-learning using a standard ConvNet pre-trained on ImageNet with this notebook
• Practice unsupervised deep learning with auto-encoders and GAN with this notebook Beginners should work on TP1 (LeNet on MNIST), and then at least begin TP3 (Deep Generative Models) Students who have already practised with Deep ConvNets should work essentially on TP3. TP2 may be useful only for those who have never practised Transfer Learning.

Day 3: Deep reinforcement learning

Instructions in this pdf (download it to use the links inside). The notebook is here. We strongly recommend using google colab (you might have to change a few cells and install a ton of dependencies if you run it locally).

Day 4: Stochastic gradient descent

Work on notebook 4_stochastic_gradient_descent.ipynb.