Deep Learning (PyTorch)
The planned outline of 18 modules. Lessons are written, run and reviewed before they are published.
Before this track
Planned outline
- Module 1
Neural network basics
Coming soon - Module 2
Backpropagation from scratch (NumPy)
Coming soon - Module 3
PyTorch tensors and autograd
Coming soon - Module 4
Training loops
Coming soon - Module 5
Convolutional neural networks (CNNs)
Coming soon - Module 6
Sequence models
Coming soon - Module 7
Transformers
Coming soon - Module 8
Graph neural networks (GNNs)
Coming soon - Module 9
Regularisation and optimisation
Coming soon - Module 10
Debugging training runs
Coming soon - Module 11
Interpretability: explaining model predictions
Coming soon - Module 12
Transfer learning
Coming soon - Module 13
Efficient training: precision, memory and speed
Coming soon - Module 14
Multi-GPU training: DDP and FSDP
Coming soon - Module 15
Generative models: from autoencoders to flow matching
Coming soon - Module 16
Audio and speech models
Coming soon - Module 17
Deployment with ONNX
Coming soon - Module 18
TensorFlow and Keras 3 bridge
Coming soon
Official documentation
- pytorch.org/docs/stable/index.html (pytorch.org)
- pytorch.org/tutorials (pytorch.org)
- d2l.ai (d2l.ai)
More in AI and machine learning
- Machine Learning (scikit-learn) (coming soon)
- AI Literacy & Prompting (coming soon)
- Building LLM Applications (coming soon)
- Artificial Intelligence (classical AI and soft computing) (coming soon)
- All AI & ML tracks
- How we make lessons