Subject
7 entries
Courses
Bookmarks
fast.ai
fast.ai is Jeremy Howard and Rachel Thomas's free deep learning course and library, famous for teaching neural networks top-down — use them effectively first, understand the math later. Widely credited with democratizing deep learning education.
Deep Learning Systems (CMU 10-414/714)
10-414/714: Deep Learning Systems at CMU — a publicly available course on building the components of a deep learning framework from scratch, including automatic differentiation, optimization, and hardware acceleration. One of the best resources for understanding how frameworks like PyTorch actually work.
Made With ML
Made With ML is a free, project-oriented ML curriculum covering the full stack from data to deployment — written by Goku Mohandas. One of the most practical ML learning resources available, because it treats production concerns as first-class alongside model building.
CS 7646: Machine Learning for Trading
CS 7646 is Georgia Tech's OMSCS course applying machine learning to financial trading — covering technical indicators, reinforcement learning for portfolio management, and backtesting. One of the few accredited graduate courses that bridges quant finance and ML.
CMU 15-721: Advanced Database Systems
CMU 15-721 is Andy Pavlo's advanced database systems course covering in-memory databases, query compilation, concurrency control, and storage engines — the internals that most engineers never see. Free lectures, reading list of seminal papers, and a reputation as one of the best systems courses available.
CS231n: Convolutional Neural Networks for Visual Recognition
Stanford CS231n: Convolutional Neural Networks for Visual Recognition — Andrej Karpathy's course that became the de facto entry point into deep learning for computer vision. The lecture notes remain among the best written explanations of CNNs, backprop, and training practice.
A List of Bioinformatics Courses
MSU's C. Titus Brown maintained this list of bioinformatics courses as a community resource during the period when academic bioinformatics was just starting to formalize. A map of where to learn computational biology before MOOCs dominated.