Tutorial 1: Python, numpy and friends 🐍
Environment setup, jupyter, python and numpy basics
Tutorials
All tutorial materials are available from the course tutorials repo. We recommend cloning this repo and pulling updates from it if you want to run the notebooks yourself.
Additionally, the tutorial notebooks can be viewed in your browser by using nbviewer.
Tutorial 1: Python,
Tutorial 2: Logistic regression
Supervised learning context, binary and multiclass logistic regression
Tutorial 3: Multilayer Perceptron
MLP model, activations, backprop, loss functions and optimization in PyTorch
Tutorial 4: Convolutional Neural Nets
Convolutional and pooling layers, architectures, spatial classification, residual nets.
Tutorial 5: Recurrent neural networks
RNN implementation, sentiment analysis, attention.
Tutorial 6: Transfer learning and domain adaptation
Transfer learning definition, contexts, fine-tuning pre-trained models, unsupervised domain adaptation.
Tutorial 7: Deep reinforcement learning
The RL setting, openAI Gym, Deep q-learning for Atari games.
Tutorial 8: Geometric deep learning
Filters on graphs, graph convolution layers, semi-supervised node classification