Tutorial 1: Python and tensor basics
Environment setup, jupyter, python, tensor basics with numpy and PyTorch
Tutorials
All tutorial materials will be available on this page.
Additionally, the tutorial notebooks can be viewed in your browser by using nbviewer.
Tutorial 2: Supervised Learning
Supervised learning framework, binary and multiclass logistic regression, pytorch and autograd basics
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: Sequence Models
RNN implementation, sentiment analysis, Temporal Convolution Networks.
Tutorial 6: Transfer learning and domain adaptation
Transfer learning definition and contexts, fine-tuning pre-trained models, unsupervised domain adaptation via an adversarial approach.
Tutorial 7: Attention
Attention mechanisms, sequence-to-sequence models, machine translation.
Tutorial 8: Deep reinforcement learning
The RL setting, openAI Gym, Deep q-learning for Atari games.
Tutorial 9: Geometric deep learning
Filters on graphs, graph convolution layers, semi-supervised node classification
Tutorial 10: CUDA Kernels
The CUDA programming model, numba, implementing CUDA kernels in python, thread synchronization, shared memory