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<!DOCTYPE html>
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<title>AI Learning Resources - Just-in-Time Learning</title>
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<h1><a href="/">Just-in-Time Learning</a></h1>
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<h1>AI Learning Resources</h1>
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<h2 id="what-is-this">What is this?</h2>
<p>This document provides a curated list of high-quality sources for learning about AI, data science, and related topics. These sources come from respected experts, researchers, and institutions, making them reliable and informative references. 📚 indicates an authoritative reference source.</p>
<h2 id="books">Books</h2>
<ul>
<li>📚 <a href="https://ml-science-book.com/">Supervised Machine Learning for Science</a> is a comprehensive book that explores the role of supervised machine learning in scientific research. It provides a philosophical justification for using ML in science and discusses best practices for integrating it effectively.</li>
<li>📚 <a href="https://cs.gmu.edu/~sean/book/metaheuristics/">Essentials of Metaheuristics</a>, an open set of lecture notes on metaheuristics algorithms, intended for undergraduate students, practitioners, programmers, and other non-experts. A metaheuristic is any stochastic optimization algorithm intended to be the last resort before giving up and using random or brute-force search. </li>
<li>📚 <a href="https://leanpub.com/ollama">Ollama in Action: Building Safe, Private AI with LLMs, Function Calling and Agents</a> demonstrates how it's possible to run LLMs on-premise to maintain data privacy and control of your tech stack. It contains clear Python code examples and recommendations for effectively using Ollama, to get you started quickly as well as learn a few useful tips and techniques.</li>
<li>📚 <a href="https://ravinkumar.com/GenAiGuidebook/book_intro.html">The GenAI Guidebook</a> constitutes a guide that lays out the foundations for building products with Generative AI.</li>
</ul>
<h2 id="courses">Courses</h2>
<ul>
<li>📚 fast.ai's <a href="https://course.fast.ai/">Practical Deep Learning</a> teaches how to apply deep learning to practical problems. Not only it is a rich, practical-oriented course but importantly it's a treasure trove of technical information.<br><a href="https://github.com/fastai/fastbook">Fastbook</a> nicely complements this course. </li>
<li>📚 <a href="https://xcancel.com/hugobowne/status/1870978490574704812">Building LLM Applications for Data Scientists and Software Engineers</a> teaches how to build LLM-powered software reliably, from first principles. It's an authoritative source on GenAI software development lifecycle: agents, evals, iteration and others. </li>
<li>📚 <a href="https://rdi.berkeley.edu/llm-agents/f24">CS294/194-196 Large Language Model Agents</a> is a course that discusses fundamental concepts for LLM agents, including the foundation of LLMs, essential LLM abilities required for task automation, and agent development infrastructure. Agent applications are discussed as well as limitations, potential risks and directions for further improvement.</li>
</ul>
<h2 id="data-driven-insights">Data-driven Insights</h2>
<ul>
<li><a href="https://ourworldindata.org/">Our World in Data</a> is a project of the Global Change Data Lab at the University of Oxford. It provides well-researched and data-driven insights into global issues, making it a valuable resource for understanding the world's largest problems and potential solutions.</li>
</ul>
<h2 id="newsletters">Newsletters</h2>
<ul>
<li><a href="https://buttondown.com/ainews/archive/">AI News</a>, a daily newsletter that summarizes the top discussions happening in AI-focused online communities. It covers the latest trends, research, and developments in the field of artificial intelligence. </li>
<li><a href="https://aibyhand.substack.com/">AI by Hand</a>, a newsletter by Tom Yeh, a professor of computer science who focuses on AI and machine learning. It provides insights and educational content providing accessible explanations of AI concepts.</li>
</ul>
<h2 id="research">Research</h2>
<ul>
<li><a href="https://www.emergentmind.com/">Emergent Mind</a> is an AI research assistant that synthesizes the latest computer science research from arXiv. It is also a paper aggregator that surfaces trending pre-prints to keep informed about new and emerging research directions.</li>
<li><a href="https://www.latent.space/p/2025-papers">The 2025 AI Engineer Reading List</a> contains 50 paper/models/blogs across 10 fields in AI Eng: LLMs, Benchmarks, Prompting, RAG, Agents, CodeGen, Vision, Voice, Diffusion, Finetuning. A good place to start.</li>
</ul>
<h2 id="social-media">Social Media</h2>
<ul>
<li><p><a href="https://bsky.app/profile/erictopol.bsky.social">Eric Topol</a> is a renowned cardiologist, scientist, and author who has written extensively on the future of medicine, including the impact of AI and digital technologies. His books and online presence are valuable resources for understanding the intersection of healthcare and technology.</p>
</li>
<li><p><a href="https://bsky.app/profile/howard.fm">Jeremy Howard</a> is a prominent data scientist, entrepreneur, and educator. He co-founded <a href="https://www.fast.ai/">fast.ai</a>, where he teaches introductory courses and conducts research in deep learning. He has also co-founded <a href="https://www.answer.ai/">answer.ai</a>, a new kind of AI R&D lab. His work and online content are highly regarded in the data science community.</p>
</li>
<li><p><a href="https://bsky.app/profile/math-rachel.bsky.social">Rachel Thomas</a> is a prominent data scientist, educator, and ethics advocate who co-founded fast.ai and served as founding director of USF's Center for Applied Data Ethics. Her work spans mathematics, AI education, and ethical tech development, earning her recognition as one of Forbes' 20 Incredible Women in AI, while she currently pursues advanced studies in microbiology-immunology.</p>
</li>
<li><p><a href="https://xcancel.com/karpathy">Andrej Karpathy</a> is a leading AI researcher and educator. He previously served as the Director of AI at Tesla and was part of the founding team at OpenAI. His online presence and educational materials provide in-depth insights into the latest advancements in AI.</p>
</li>
<li><p><a href="https://bsky.app/profile/simonwillison.net">Simon Willison</a>, an independent open-source developer and a data leader who has made significant contributions to the open-source community. He is the creator of <a href="https://simonwillison.net/2022/Mar/1/datasette/">Datasette</a>, a tool for exploring and publishing data, and a co-creator of the <a href="https://www.djangoproject.com/">Django</a> web framework.</p>
</li>
<li><p><a href="https://bsky.app/profile/melaniemitchell.bsky.social">Melanie Mitchell</a> is a respected professor of computer science and a prominent figure in the field of complex systems and artificial intelligence. Her research and publications offer valuable perspectives on the challenges and opportunities in AI.</p>
</li>
<li><p><a href="https://bsky.app/profile/maosbot.bsky.social/post/3laix6hz42n2m">Michael A. Osborne</a> is a professor of machine learning who focuses on Bayesian optimisation and its applications within battery modelling and tuning quantum devices. He has created Bluesky starter packs distilling prominent figures in relevant fields that are worth following. </p>
</li>
<li><p><a href="https://bsky.app/profile/christophmolnar.bsky.social">Christoph Molnar</a> is a statistician, machine learning expert, and author specializing in ML interpretability and uncertainty quantification. He wrote the influential books Interpretable Machine Learning and Modeling Mindsets, focusing on translating academic research into practical data science insights. He publishes the Mindful Modeler newsletter and is known for making complex ML concepts accessible to practitioners.</p>
</li>
</ul>
<h2 id="talks">Talks</h2>
<ul>
<li><a href="https://dotclub.club/">The .CLUB Club</a>, offering insightful talks on egoless engineering, straightforward tech solutions, and workplace politics as seen from an engineering leadership perspective (highly recommended!). </li>
<li><a href="https://media.ccc.de/">ccc.de</a> hosts a vast collection of presentations by the Chaos Computer Club, Europe's largest hacker association. Topics range from reverse engineering to cutting-edge exploration and more, making it an invaluable source of practical ideas, scientific exploration, and authentic engineering concepts.</li>
<li><a href="https://www.youtube.com/channel/UCJgIbYl6C5no72a0NUAPcTA">GPU MODE</a> contains excellent and rich material on GPU programming, including CUDA kernels, Flash Attention, Triton, quantisation among others.</li>
</ul>
<h2 id="articles">Articles</h2>
<ul>
<li><a href="https://venturebeat.com/ai/mlops-vs-devops-why-data-makes-it-different/">MLOps vs. DevOps: Why data makes it different</a> explains how machine learning operations (MLOps) differ from traditional DevOps due to ML's direct exposure to messy real-world data, requiring specialised infrastructure that combines data-centric programming with robust production systems. </li>
<li>📚 <a href="https://hamel.dev/blog/posts/evals/">Your AI Product Needs Evals</a> is a very nicely structured, comprehensive resource on how to construct domain-specific evaluation systems. </li>
<li>📚 <a href="https://www.sh-reya.com/blog/ai-engineering-flywheel/">Data Flywheels for LLM Applications</a> breaks down how to improve AI systems in an iterative fashion, with the help of logging, evals and continuous refinement. </li>
<li>📚 <a href="https://applied-llms.org/">What We’ve Learned From A Year of Building with LLMs</a> is a practical guide to building successful LLM products, covering the tactical, operational, and strategic.<br>The article's authors provided their insights in the Vanishing Gradients podcast (episodes <a href="https://vanishinggradients.fireside.fm/29">29</a> and <a href="https://vanishinggradients.fireside.fm/30">30</a>). </li>
<li>📚 <a href="https://www.anthropic.com/engineering/building-effective-agents">Building effective agents</a> is a research paper from Anthropic, exploring how to design robust AI agents. It presents agentic workflow approaches, alignment and methods on how to perform autonomous, reliable tasks.</li>
<li>📚 <a href="https://vickiboykis.com/what_are_embeddings/">What are Embeddings</a> is a comprehensive article exploring embeddings.</li>
<li>📚 <a href="https://www.boundaryml.com/blog/structured-output-from-llms">Every Way To Get Structured Output From LLMs</a> provides solutions for achieving LLM structured output when function calling or specific response formats become challenging to achieve.</li>
</ul>
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