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

Tutorial 13: Bayesian Deep Learning
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Tutorial 13: Bayesian Deep Learning

Status

Filled notebook:  
Empty notebook:  
Pre-trained models:

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© Copyright 2022, Phillip Lippe.

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