# AINS6003 Deep Learning & Neural Networks

This book is the instructional hub for **AINS6003 Deep Learning & Neural Networks** (Aurnova MSAI core course **AIN6003**).

It is organized as one unified MyST Jupyter Book so learners and instructors navigate a single artifact:

- book prose for conceptual understanding
- assignments for applied practice (Thebe-enabled notebooks)
- slides for presentation flow (RISE-ready notebooks)
- narration for asynchronous delivery
- instructor notes for teaching guidance
- notebooks for executable exploration

## Course arc (8 modules)

1. From neurons to multilayer networks
2. Backpropagation and automatic differentiation
3. Optimization, loss, and regularization
4. Convolutional neural networks for vision
5. Sequence models: RNNs and LSTMs
6. Attention and transformers
7. Generative models and applications
8. GPU workflows, scale, and deployment

GPU-backed labs are scaffolded for Modules 3–8. Students should use the course Codespace or approved cloud GPU environment where noted in the syllabus.