Explore, build, and refine codebases with Claude Code.

Build, debug, and optimize AI agents using DSPy and MLflow.

A practical handbook for engineers building, optimizing, scaling and operating LLM inference systems in production.

FAQ from our course on AI Evals.

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Anthropic's Interactive Prompt Engineering Tutorial - anthropics/prompt-eng-interactive-tutorial

Reinforcement Learning has helped train neural networks to win games, drive cars and even get ChatGPT to sound more human when it responds to your prompt. Th...

Shortform link: https://shortform.com/artem In this video we will talk about backpropagation – an algorithm powering the entire field of machine learning and try to derive it from first principles. OUTLINE: 00:00 Introduction 01:28 Historical background 02:50 Curve Fitting problem 06:26 Random vs guided adjustments 09:43 Derivatives 14:34 Gradient Descent 16:23 Higher dimensions 21:36 Chain Rule Intuition 27:01 Computational Graph and Autodiff 36:24 Summary 38:16 Shortform 39:20 Outro USEFUL RESOURCES: Andrej Karpathy's playlist: https://youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ&si=zBUZW5kufVPLVy9E Jürgen Schmidhuber's blog on the history of backprop: https://people.idsia.ch/~juergen/who-invented-backpropagation.html CREDITS: Icons by https://www.freepik.com/

Methods and Strategies for Building and Refining Reasoning Models

An overview of the papers describing the evolution of DeepSeek

Make your own AI by hand ✍️ exercises

A library for graph deep learning research. Contribute to divelab/DIG development by creating an account on GitHub.

Finetuning a GPT Model for Spam Classification

LLM101n: Let's build a Storyteller. Contribute to karpathy/LLM101n development by creating an account on GitHub.

Demystifying attention, the key mechanism inside transformers and LLMs.Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3...

An introduction to transformers and their prerequisitesEarly view of the next chapter for patrons: https://3b1b.co/early-attentionOther recommended resources...

A spreadsheet implementation of GPT2 for understanding AI, Machine Learning, Transformers, and Large Language Models

Language models have shown impressive capabilities. But that doesn’t mean they’re without faults, as anyone who has witnessed a ChatGPT “hallucination” can attest. In this article, Joas Pambou diagnoses the symptoms that cause hallucinations and explains not only what RAG is but also different approaches for using it to solve language model limitations.

Implementing a ChatGPT-like LLM from scratch, step by step - rasbt/LLMs-from-scratch: Implementing a ChatGPT-like LLM from scratch, step by step

Colab 01. Learning LangGraph Agent Executor: https://drp.li/vL1J9 Colab 02. Learning LangGraph - Chat Executor: https://drp.li/HAz3o Colab 03. Learning LangGraph - Agent Supervisor: https://drp.li/xvEwd Interested in building LLM Agents? Fill out the form below Building LLM Agents Form: https://drp.li/dIMes Github: https://github.com/samwit/langchain-tutorials (updated) https://github.com/samwit/llm-tutorials Time Stamps: 00:00 Intro 00:19 What is LangGraph? 00:26 LangGraph Blog 01:38 StateGraph 02:16 Nodes 02:42 Edges 03:48 Compiling the Graph 05:23 Code Time 05:34 Agent with new create_open_ai 21:37 Chat Executor 27:00 Agent Supervisor

LoRA (Low-Rank Adaptation) is a popular technique to finetune LLMs more efficiently. This Studio explains how LoRA works by coding it from scratch, which is an excellent exercise for looking under the hood of an algorithm.

Machine Learning Engineering Open Book. Contribute to stas00/ml-engineering development by creating an account on GitHub.

AI/ML has been witnessing a rapid acceleration in model improvement in the last few years. The majority of the state-of-the-art models in the field are based on the Transformer architecture. Examples include models like BERT (which when applied to Google Search, resulted in what Google calls "one of the biggest leaps forward in the history of Search") and OpenAI's GPT2 and GPT3 (which are able to generate coherent text and essays). This video by the author of the popular "Illustrated Transformer" guide will introduce the Transformer architecture and its various applications. This is a visual presentation accessible to people with various levels of ML experience. Intro (0:00) The Architecture of the Transformer (4:18) Model Training (7:11) Transformer LM Component 1: FFNN (10:01) Transformer LM Component 2: Self-Attention(12:27) Tokenization: Words to Token Ids (14:59) Embedding: Breathe meaning into tokens (19:42) Projecting the Output: Turning Computation into Language (24:11) Final Note: Visualizing Probabilities (25:51) The Illustrated Transformer: https://jalammar.github.io/illustrated-transformer/ Simple transformer language model notebook: https://github.com/jalammar/jalammar.github.io/blob/master/notebooks/Simple_Transformer_Language_Model.ipynb Philosophers On GPT-3 (updated with replies by GPT-3): https://dailynous.com/2020/07/30/philosophers-gpt-3/ ----- Twitter: https://twitter.com/JayAlammar Blog: https://jalammar.github.io/ Mailing List: https://jayalammar.substack.com/ More videos by Jay: Jay's Visual Intro to AI https://www.youtube.com/watch?v=mSTCzNgDJy4 How GPT-3 Works - Easily Explained with Animations https://www.youtube.com/watch?v=MQnJZuBGmSQ