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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.
A Twitter user shares a 185-page deep learning book covering foundations, deep models, architectures, applications, and compute schism topics via a Google Drive link.
A structured 19-phase AI/ML learning curriculum covering topics from setup and math to capstone projects, created by @ghumare64.
Hugging Face shared slides detailing how they generated 1 trillion tokens of synthetic data for training foundation models.
MIT has made the entire 'Deep Learning' textbook by Goodfellow, Bengio, and Courville freely available online without paywall or signup, providing over 800 pages of foundational knowledge.
Stanford provides an explanatory document on Artificial Intelligence & Machine Learning, available via Google Drive.
The Awesome-SpeechLM-Survey repository on GitHub systematically organizes the research lineage of speech language models, including classification frameworks, representative models, training datasets, and evaluation benchmarks. It serves as a knowledge map for understanding the field.
Ollie Forsyth released an ultimate creator map covering new media creators in the tech field, emphasizing the importance of attention, distribution, influence, and taste in building future media.