UvA DL Notebooks

Guides

  • Guide 1: Working with the Snellius cluster
  • Guide 2: Research projects with PyTorch
  • Guide 3: Debugging in PyTorch
  • Guide 4: Research Projects with JAX

Training Models at Scale

  • Overview
  • Part 1.1: Training Larger Models on a Single GPU
  • Part 1.2: Profiling and Scaling Single-GPU Transformer Models
  • Part 2.1: Introduction to Distributed Computing in JAX
  • Part 2.2: (Fully-Sharded) Data Parallelism
  • Part 3.1: Pipeline Parallelism
  • Part 3.2: Looping Pipelines
  • Part 4.1: Tensor Parallelism
  • Part 4.2: Asynchronous Linear Layers with Tensor Parallelism
  • Part 4.3: Transformers with Tensor Parallelism
  • Part 5: Language Modeling with 3D Parallelism

Deep Learning 1 (PyTorch)

  • Tutorial 2: Introduction to PyTorch
  • Tutorial 3: Activation Functions
  • Tutorial 4: Optimization and Initialization
  • Tutorial 5: Inception, ResNet and DenseNet
  • Tutorial 6: Transformers and Multi-Head Attention
  • Tutorial 7: Graph Neural Networks
  • Tutorial 8: Deep Energy-Based Generative Models
  • Tutorial 9: Deep Autoencoders
  • Tutorial 10: Adversarial attacks
  • Tutorial 11: Normalizing Flows for image modeling
  • Tutorial 12: Autoregressive Image Modeling
  • Tutorial 15: Vision Transformers
  • Tutorial 16: Meta-Learning - Learning to Learn
  • Tutorial 17: Self-Supervised Contrastive Learning with SimCLR

Deep Learning 1 (JAX+Flax)

  • Tutorial 2 (JAX): Introduction to JAX+Flax
  • Tutorial 3 (JAX): Activation Functions
  • Tutorial 4 (JAX): Optimization and Initialization
  • Tutorial 5 (JAX): Inception, ResNet and DenseNet
  • Tutorial 6 (JAX): Transformers and Multi-Head Attention
  • Tutorial 7 (JAX): Graph Neural Networks
  • Tutorial 9 (JAX): Deep Autoencoders
  • Tutorial 11 (JAX): Normalizing Flows for image modeling
  • Tutorial 12 (JAX): Autoregressive Image Modeling
  • Tutorial 15 (JAX): Vision Transformers
  • Tutorial 17 (JAX): Self-Supervised Contrastive Learning with SimCLR

Deep Learning 2

  • GDL - Regular Group Convolutions
  • GDL - Steerable CNNs
  • DPM1 - Deep Probabilistic Models I
  • DPM2 - Variational inference for deep discrete latent variable models
  • DPM 2 - Variational Inference for Deep Continuous LVMs
  • AGM - Advanced Topics in Normalizing Flows - 1x1 convolution
  • HDL - Introduction to HyperParameter Tuning
  • HDL - Introduction to Multi GPU Programming
  • Tutorial 1: Bayesian Neural Networks with Pyro
  • Tutorial 2: Comparison to other methods of uncertainty quantification
  • DNN - Tutorial 2 Part I: Physics inspired Machine Learning
  • DNN - Tutorial 2 Part II: Physics inspired Machine Learning
  • DS - Dynamical Systems & Neural ODEs
  • SGA - Sampling Discrete Structures
  • SGA - Sampling Subsets with Gumbel-Top \(k\) Relaxations
  • SGA: Learning Latent Permutations with Gumbel-Sinkhorn Networks
  • SGA - Graph Sampling for Neural Relational Inference
  • CRL - Causal Identifiability from Temporal Intervened Sequences

Work in Progress Pure PyTorch

  • Work in Progress for the new PyTorch version
UvA DL Notebooks
  • Search

© Copyright 2022, Phillip Lippe.

Built with Sphinx using a theme provided by Read the Docs.