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ashishpatel26/Andrew-NG-Notes
This is Andrew NG Coursera Handwritten Notes.
A community-maintained collection of handwritten notes and original Coursera notebooks from Andrew Ng's Machine Learning and Deep Learning Specialization courses. It's a reference archive for people working through those courses, not a standalone learning resource. If you haven't taken the courses, these notes will be confusing without the lecture context.
The Markdown note files (andrewng-p-1 through p-5) are well-organized and cover the full specialization arc from basic neural networks through RNNs and attention. The original Coursera notebooks are included intact, so you get runnable implementations of logistic regression, ResNets, YOLO object detection, and neural style transfer without needing to pay for Coursera access. Links to the foundational papers (AlexNet, ResNet, YOLO, FaceNet) alongside the relevant notebooks is a genuinely useful pairing.
The course content is from 2017-2018 and the repo hasn't been meaningfully updated since — the deep learning landscape has shifted substantially since then (transformers are barely mentioned, the sequence models section predates BERT). The notebooks use old Keras/TensorFlow 1.x patterns and some won't run without environment archaeology. Week links in the README are inconsistent — some point to nbviewer, some to GitHub raw files, some to a completely different repo (ashishpatel26/Deep-Learning-Coursera), which breaks the experience. There's no requirements.txt or environment spec, so actually running these locally is trial and error.