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Deep learning with PyTorch
Deep learning with PyTorch
A deep learning course with
AI
Intro DL with PyTorch
Introduction
Slides content
Which framework to choose?
High-level frameworks
Introduction to NN
Slides content
The PyTorch API
PyTorch tensors
Automatic differentiation
Workflow
Creating checkpoints
Example: the MNIST
DL on production clusters
Resources
Deep learning with PyTorch
The JAX library
Why JAX?
How does it work?
Installation
Running jobs
Relation to NumPy
JIT compilation
Benchmarking JAX code
Accelerators
Pytrees
Automatic differentiation
Parallel computing
Pushing optimizations further
Libraries built on JAX
Resources
Intro DL with JAX
Introduction: JAX
Slides content
Running JAX
Installing packages
Relation to NumPy
Loading data
Preprocessing data
Defining a model architecture
Loading pre-trained weights
Hyperparameters
Fine-tuning the model
Training at scale
Resources
DL with the JAX AI stack
The JAX stack
Slides content
Our deep learning example
Installing packages
Getting the data
Temporary JupyterHub
Compiling the metadata
Data preprocessing
DataLoaders
Data augmentation
Model and training strategy
Training
Using the model for inference
Next steps
Resources
ML with scikit-learn
What is scikit-learn?
Sklearn workflow
Methods in NLP
Notes about GPUs
Webinars
AI-powered coding
Slides content
Data & model version control
Accelerated array & AD
Slides content
ML experiment tracking
Slides content
Current ML frameworks
Slides content
Image upscaling
Slides content
Bayesian inference in JAX
Slides content
DL in Julia with Flux
PyTorch tensors in depth
Slides content
Easier PyTorch with fastai
Workshops
Audio DataLoader with PyTorch
Finding pre-trained models
Intro ML for the humanities
Slides content
Intro to DL, NLP, and LLMs
Slides content
Coming up on November 26, 2026.
