Playlists

• Machine Learning Specialization (Andrew Ng)

  My step-by-step notes going through Andrew Ng's Machine Learning Specialization (DeepLearning.AI / Stanford), course by course, lab by lab.

• Pattern Recognition

  My notes from my Pattern Recognition course, lecture by lecture, with Bishop's book as the theoretical backbone.

• Neural Networks

  My notes from my Neural Networks course, lecture by lecture, with Aggarwal's book as the theoretical backbone. A living playlist, growing along with the course.

• Papers

  A diagonal read of articles and surveys I found cool, with no pretense of turning into a lecture. Simple code, a chart to give you a view, and the same conversation as always with you.

• Small Language Models

  Chapter by chapter through "How to Build and Fine-Tune a Small Language Model", by J. Paul Liu. Why train your own model instead of outsourcing everything to an API, and how to build a GPT from scratch until it writes like a 19th-century Brazilian novelist.