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A free 674-page PDF of the textbook 'Introduction to Machine Learning' by Laurent Younes is available, covering mathematical foundations and algorithms for machine learning.
Fonts In Use is a website that helps designers identify and explore where specific fonts are used in real-world designs, such as album art and posters.
Michael Nielsen's free online book 'Neural Networks and Deep Learning' teaches neural networks from first principles with browser-based code, covering key topics like backpropagation and convolutional networks.
MIT offers the free textbook 'Structure and Interpretation of Computer Programs' (SICP) for learning computer science fundamentals like recursion, abstraction, and language design.
Recommends the Transformer chapter from the freely available book Speech and Language Processing by Dan Jurafsky and James H. Martin, a top resource for understanding the mathematics behind LLMs, and includes a quoted explanation of LLM pre-training.
A GitHub repository implementing fundamental machine learning algorithms from scratch using plain NumPy, designed to help learners understand the inner workings of algorithms by focusing on clarity over performance. It covers supervised, unsupervised, deep learning, and reinforcement learning topics.
Someone has organized the entire agent workflow of Fable 5 into 9 images, which can be directly fed to Claude for use, making it convenient and efficient.
A tweet recommending The Annotated Transformer as a resource for software engineers to understand the foundational concepts of the Attention Is All You Need paper by building it in PyTorch.
Recommending PhD Stack (csphd.org), a public-interest platform maintained by an assistant professor at a U.S. university, providing end-to-end support and community engagement for CS/AI/EE/Stats PhD students from enrollment to career.
A tweet promoting a curated learning path covering key AI engineering concepts, claiming a personal BSc-equivalent education in 3 weeks.
Niels Rogge added Lilian Weng's blog on scaling laws as a recommended read on Papers with Code, linking to the original paper and citations.
A Twitter user recommends a comprehensive book on generative AI covering language modeling, inference optimization, RL, system scaling, and applied concepts like agentic AI and RAG, also sharing advice to read top-cited papers from Papers With Code.
Nathan Lambert announces his goal to create a comprehensive hub for learning RLHF post-training, including a book, lectures, code, and community resources.
A curated page on Papers with Code lists top open-source OCR models and benchmarks, highlighting new releases from Baidu (Unlimited OCR) and Mistral (OCR 4), aimed at enabling AI agent use cases like RAG.
A thread sharing links to ebooks on AI SOC Mastery (100+ pages) and Catatan AI SOC (600+ pages), including PDFs and source code, available on Google Drive.
A tweet recommends a lecture by an OpenAI researcher on how LLMs are built, claiming it taught an MIT CS grad more than his entire degree.
awesome-autoresearch is a curated list of automated research use cases. This update adds two entries: Cribl's production deployment and Colab TPU port.
Wang Ray translated the Claude Certified Architect exam guide into a Chinese version (28-page PDF) and uploaded it to his knowledge base for exam takers' reference.
A carefully curated collection of papers related to large model systems, covering training, inference, multimodality, and more. It is continuously updated and includes technical reports, frameworks, and courses, making it a valuable reference for researchers and developers.
Linear design engineer Emil Kowalski compiled 90+ animation terms covering 12 categories. The author used Claude to create a bilingual preview site for understanding the effects.