AI
Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.
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Probabilistic Machine Learning probml.github.io
Introductory and advanced books by Kevin Murphy, with free draft PDFs, code, figures, exercises, and teaching resources.
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Mathematics of Machine Learning ocw.mit.edu
Free graduate MIT course on the mathematical and statistical foundations of machine learning, with lecture notes, assignments, and problem-set solutions.
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Mathematics for Machine Learning mml-book.github.io
Free textbook on the linear algebra, geometry, calculus, probability, and optimization needed to study machine learning.
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Reinforcement Learning: An Introduction mitpress.mit.edu
Richard S. Sutton, Andrew G. Barto (2nd edition).
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Math AI Seminar math.washington.edu
University of Washington research seminar on formalization, theorem proving, mathematical AI, and machine-learning applications in mathematics, with a multi-year event archive.
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LeanDojo v2 leandojo.org
Open-source framework and benchmark data for training, evaluating, and deploying AI-assisted theorem provers for Lean 4.
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b=M²L bm2l.github.io
Barcelona Mathematics and Machine Learning colloquia on the interaction between mathematics and machine learning, with multi-year editions and recorded talks.
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First Proof Project 1stproof.org
Independent project that publishes research-level problems, evaluation methods, solutions, and expert commentary for assessing AI systems in mathematics.
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Karpathy's Neural Networks: Zero to Hero youtube.com
Build neural networks and language models from first principles.
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Speech and Language Processing web.stanford.edu
The continuously updated NLP reference by Dan Jurafsky and James Martin.
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Reinforcement Learning: An Introduction web.stanford.edu
Sutton and Barto's foundational treatment of reinforcement learning concepts and algorithms.
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Understanding Deep Learning udlbook.github.io
Theory, intuition, and practical notebooks by Simon Prince.
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The 100-Page Language Models Book thelmbook.com
A concise, technical introduction by Andriy Burkov.
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Promptfoo promptfoo.dev
Test cases, assertions, model comparisons, and red-team checks for LLM applications.
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LLM Engineer's Handbook packtpub.com
LLMOps, fine-tuning, serving, and production workflows.
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Designing Machine Learning Systems oreilly.com
Scalable, maintainable machine learning systems by Chip Huyen.
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AI Engineering oreilly.com
Building applications with foundation models by Chip Huyen.
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Running Agents openai.github.io
Lifecycle, session, exception, and durable-execution patterns in the OpenAI Agents SDK.
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Human-in-the-Loop openai.github.io
Pause, inspect, approve, reject, and resume tool calls without losing agent state.
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OpenAI Agents SDK openai.github.io
A small SDK for tools, handoffs, guardrails, tracing, and agent orchestration.
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Harness Engineering openai.com
OpenAI's field report on building software with coding agents, repository constraints, automated checks, and human steering.
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Model Context Protocol modelcontextprotocol.io
The open specification for connecting AI applications to external tools, data sources, prompts, and interactive apps.
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Made With ML madewithml.com
An open course on designing, developing, deploying, and iterating on production ML systems.
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Hands-On Large Language Models llm-book.com
A visual and practical guide by Jay Alammar and Maarten Grootendorst.
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Microsoft Agent Framework learn.microsoft.com
Microsoft's successor to AutoGen and Semantic Kernel for agents and graph-based workflows.
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DeepLearning.AI Short Courses learn.deeplearning.ai
Focused courses on current generative AI engineering techniques.
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Langfuse langfuse.com
Tracing, evaluation, prompt management, and metrics for LLM applications.
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MIT 6.S191: Introduction to Deep Learning introtodeeplearning.com
Deep learning foundations and applications.
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Hugging Face LLM Course huggingface.co
Transformers, fine-tuning, datasets, and modern NLP tooling.
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Google Agent Development Kit google.github.io
Google's framework for developing and evaluating agents.
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OWASP Top 10 for LLM Applications genai.owasp.org
Risks and mitigations for developing and deploying generative AI applications.
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Full Stack Deep Learning fullstackdeeplearning.com
The full lifecycle of building and shipping AI products.
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vLLM docs.vllm.ai
An inference and serving engine for language models.
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Ragas docs.ragas.io
Evaluation and experimentation for retrieval and generative AI applications.
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LlamaIndex docs.llamaindex.ai
Data ingestion, indexing, retrieval, and agent workflows.
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LiteLLM docs.litellm.ai
A model gateway and unified interface for multiple model providers.
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Haystack docs.haystack.deepset.ai
Modular pipelines for retrieval and generative AI applications.
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Anthropic Prompt Engineering docs.anthropic.com
Techniques for defining success criteria, testing prompts, and improving model behaviour.
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Stanford CS336: Language Modeling from Scratch cs336.stanford.edu
Build language models from data preparation through evaluation and deployment.
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OpenAI Cookbook cookbook.openai.com
Code examples for structured outputs, tool use, retrieval, evals, and other LLM application patterns.
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Google Generative AI Learning Path cloudskillsboost.google
An introductory path through generative AI concepts and Google Cloud tooling.
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A Practical Guide to Building Agents cdn.openai.com
OpenAI's guide to models, tools, instructions, orchestration, and guardrails.
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Constitutional AI arxiv.org
A method for training helpful and harmless AI assistants using written principles.
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ReAct arxiv.org
Combined reasoning traces with actions for tool-using language-model agents.
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Training Compute-Optimal Large Language Models arxiv.org
Showed how model size and training data should scale together under a compute budget.
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Training Language Models to Follow Instructions with Human Feedback arxiv.org
Established the instruction tuning and RLHF recipe used by InstructGPT.
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LoRA arxiv.org
Introduced low-rank adaptation for parameter-efficient model fine-tuning.
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Language Models are Few-Shot Learners arxiv.org
Demonstrated in-context learning at scale.
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Retrieval-Augmented Generation arxiv.org
Combined parametric language models with external retrieval for knowledge-intensive tasks.
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Scaling Laws for Neural Language Models arxiv.org
Explored relationships between model performance, data, and compute.
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