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A recommendation for free class notes on computational mathematics (numerical analysis) by De Sterck and Ullrich, made publicly available by UC Davis, covering error propagation, root finding, interpolation, integration, Fourier methods, and numerical linear algebra.
The third edition of the Speech and Language Processing textbook by Jurafsky and Martin was released in January 2026, featuring a clear explanation of Transformers and various updates including new chapters on ASR, TTS, and DPO.
A tweet highlights the Transformer architecture chapter from Jurafsky and Martin's textbook, praising its clear and mathematically grounded explanation of self-attention, multi-head attention, and related mechanisms.
A 230-page book that comprehensively covers LLM concepts including pre-training, fine-tuning, alignment, and prompting techniques.
A 178-page survey study from the University of Huddersfield covering math and generative AI foundations, titled 'The Little Book of Generative AI Foundations'.
A free Stanford lecture on Diffusion and Vision Model Architectures is being highlighted as covering foundational knowledge that can elevate AI engineering skills to the level of top-tier compensation at Google.
Updated AI workshop with over 130 new slides covering classical ML, search algorithms, planning, knowledge graphs, agents, and practical labs, based on a university syllabus.
Think Linear Algebra is a code-first, open-source educational resource that teaches linear algebra concepts using Python and Jupyter notebooks, focusing on practical applications rather than abstract theory.
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.
This article introduces Machine Learning Visualized, a Jupyter Book and interactive platform that implements and derives machine learning algorithms from first principles with visualizations.
A user shares their redesign of the 'AI Engineering from Scratch' website, which serves as a reference manual explaining AI concepts like transformers and backpropagation from raw mathematical implementations.
The Zhejiang University team open-sourced an easy-to-understand textbook on large models 'Foundations of Large Models', covering from architectural evolution to key technologies like RAG, accompanied by the Agent-Kernel multi-agent framework.