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.