AINS6003 Deep Learning & Neural Networks

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

::{admonition} Work in GitHub Codespaces :class: tip

Launch a browser-based development environment for the full AINS6003 Deep Learning & Neural Networks repository:

Open this course in GitHub Codespaces

::{admonition} Use Google Colab for Notebook Labs :class: note

Many students in this program are new to Python. Colab is the recommended starting point for notebook labs because it opens in the browser and does not require installing Python, using a terminal, or understanding Git. Students can run the cells, change one small value, and write what they observe.

Start with Colab for labs. It gives students a notebook in the browser without requiring Git, a terminal, or local Python setup. Use Codespaces only when an instructor asks students to work with the full repository or a multi-file project.

Activity

Best Environment

Why

Open and run a module lab

Colab

Easiest path for students who have never used Python.

Change one parameter, threshold, feature, or model setting

Colab

The lab exercises are small, guided, and safe to rerun.

Write short answers or observations from a lab

Colab

Students can submit a shared Colab link or exported notebook when allowed.

Work on a larger graded project with several files

Codespaces

Keeps notebooks, scripts, data files, and instructions together.

Use GitHub, terminal commands, tests, or repository history

Codespaces

Better for instructor-guided technical workflows, not first-time Python practice.

Lab

Launch

Module 1 Lab

Open in Colab

Module 2 Lab

Open in Colab

Module 3 Lab

Open in Colab

Module 4 Lab

Open in Colab

Module 5 Lab

Open in Colab

Module 6 Lab

Open in Colab

Module 7 Lab

Open in Colab

Module 8 Lab

Open in Colab

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 can use the course Codespace or an approved cloud GPU environment where noted in the syllabus.