Deep Learning Notes

These are a set of notes that I took as I worked my way through the Deep Learning Specialization, a fabulous series of online courses on machine learning on Coursera taught by Andrew Ng.

I simply wanted to learn a little more about modern AI technologies as I started this course, but Andrew’s courses taught me the underlying principles in a way that far exceeded my expectations. I definitely give this specialisation a five-star recommendation.

It took me over five months to complete this series of courses at my own pace from October 2025 to April 2026. That included the time dedicated to the notes. The act of note-taking has been a challenging workload as well as a great pleasure, as this compelled me to reflect more deeply on the course contents and to fill in the mathematical reasoning underpinning various conclusions. By attempting to articulate the materials from my personal perspectives, rather than merely passively absorbing them, I believed I had gained a better understanding of the subject matter, and these notes might also serve as a useful reference for beginners who are just starting out.

Almost all contents in the notes, including the texts, formulas and figures are far from original. Only a small portion of the diagrams were drawn by myself, the majority of the contents were either taken from the course materials or from internet resources. I have also received much help from Claude for revising my notes. All credits of these notes and my deepest respect go to Andrew Ng and his teaching team.

Download

The complete pdf notes can be downloaded here

Links

The same set of notes have also also posted on this website. The links are provided below:

• Course 1: Neural Networks and Deep Learning
  • Week 1-3: Binary Classifiers with Logistic Regression
  • Week 4: Multi-layer Neural Networks
• Course 2: Improving Deep Neural Networks
  • Week 1-4: Hyperparameter Tuning, Regularization and Optimisation
• Course 4: Convolutional Neural Networks
  • Week 1: Convolutional Neural Network Basics
  • Week 2: Deeper Convolutional Neural Networks
  • Week 3A: Object Detection
  • Week 3B: Image Semantic Segmentation
  • Week 4A: Face Recognition
  • Week 4B: Neural Style Transfer
• Course 5: Sequence Models
  • Week 1A: Recurrent Neural Network Basics
  • Week 1B: LSTM and GRU Networks
  • Week 2: Word Embeddings
  • Week 3: Beam Search and BLEU Score
  • Week 4: Attention Mechanism and Transformers