Learning
- How I learn machine learning | ★❤✰ Vicki Boykis ★❤✰; 2022.
- Machine Learning Engineer Roadmap | /home/vigi99
- ludwigbap/ml learning resources
- Stanford CS229: Machine Learning, Spring 2022 Notes)
- Lecture Videos Playlist on Youtube Learning from Data from Caltech; Youtube, also uses the eponymous textbook
When people are getting ready to learn machine learning at MSc level, they don’t learn from a course that says “if you don’t understand it it doesn’t matter”, they learn from this course instead. Abu-Mostafa’s Caltech course is probably the best first course that delivers good understanding of machine learning. via
Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong.
Machine Learning for MacroEconomics by Jesús Fernández-Villaverde | University of Pennsylvania
Home | CS 229S; Systems for Machine Learning
Math For Machine Learning [Resources] at https://lelouch.dev
Resources
- franknielsen / Lists of curated books
- stas00/ml-engineering: Machine Learning Engineering Guides and Tools
- MAD - Machine learning, Artificial intelligence and Data; ( PDF chart of the landscape); related reading - The 2023 MAD (Machine Learning, Artificial Intelligence & Data) Landscape – Matt Turck; The interactive 2024 version
Video
- hu-po - YouTube; Livestreams on ML papers, Coding, Research.
Blogs
- Eugene Yan
- ApplyingML - Papers, Guides, and Interviews with ML practitioners by Eugene Yan
- Chip Huyen
- Leonie Monigatti, example see - 37 Things I Learned About Information Retrieval in Two Years at a Vector Database Company
Papers
Browse the State-of-the-Art in Machine Learning | Papers With Code
Pen and Paper Exercises in Machine Learning
This is a collection of (mostly) pen-and-paper exercises in machine learning. The exercises are on the following topics: linear algebra, optimisation, directed graphical models, undirected graphical models, expressive power of graphical models, factor graphs and message passing, inference for hidden Markov models, model-based learning (including ICA and unnormalised models), sampling and Monte-Carlo integration, and variational inference.
See also: machine-learning-books