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

This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field.

Description

This is the first in a series talking about Transformers. As of now, there are quite a few good resources on Transformers, so why make…

Generative AI for Constructive Communication Course

A Comprehensive Overview of Prompt Engineering

What you’ll learn in this course In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful applications.  Using the OpenAI API, you’ll...

Large Language Models (LLMs) have a data freshness problem. Even some of the most powerful models, like ChatGPT's gpt-3.5-turbo and GPT-4, have no idea about...

In this LangChain Crash Course you will learn how to build applications powered by large language models.

We’ve released our new course with over 30 hours of video content.

A complete from-scratch rewrite of fast.ai's most popular course, that's been 2 years in the making.

A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.

An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.