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Stanford's CS230 Deep Learning (Autumn 2025) course playlist is shared on YouTube, offering free access to the lectures.
Berkeley CS162 offers a free comprehensive online operating systems course with lectures, projects, and assignments covering key OS concepts like processes, virtual memory, scheduling, and distributed systems.
An online course on applied category theory taught by John Baez, based on the book 'Seven Sketches in Compositionality', covering ordered sets, resource theories, and databases through a series of lecture notes.
Gilbert Strang presents an updated vision for teaching linear algebra through a YouTube playlist and MIT OpenCourseWare site.
CS 6120 is a PhD-level online self-guided course on advanced compilers, covering intermediate representations, data flow, optimizations, and including paper readings and implementation tasks using LLVM and Bril.
MIT's Advanced Data Structures course (6.851) by Prof. Erik Demaine is available fully online with video lectures and collaborative problem solving.
Andrew Ng and DeepLearning.AI have launched a new short course on efficient LLM inference with vLLM, built in partnership with Red Hat, covering quantization, PagedAttention, continuous batching, and benchmarking for serving LLMs at scale.
Stanford University offers a free online course on mathematical methods for computer vision, robotics, and graphics, including a full PDF textbook and video lectures, making high-quality education accessible to everyone.
Stanford's CS336 course on modern neural language models, covering topics like MoEs and RLHF, is being released on YouTube with a two-week delay.
The article highlights a new free 90-minute AI class from Stanford, contrasting it with traditional university degrees in terms of cost and potential career outcomes at companies like OpenAI.
This article recommends a UCLA-led online course on Reinforcement Learning for Large Language Models, covering theory, algorithms like PPO and RLHF, and practical coding exercises.
Assistant Professor Ernest K. Ryu at UCLA offers the open course 'Reinforcement Learning for Large Language Models,' comprehensively analyzing key LLM training techniques like RLHF, PPO, and DPO alongside their supporting resources through a blend of theory and practice. The course provides developers and researchers with a systematic learning path from foundational algorithms to practical deployment.