Unpacking the multilayer perceptrons in a transformer, and how they may store facts. Grant explores the inner workings of large language models, specifically focusing on how they predict and encode information. Understanding the storage and retrieval of facts in AI relies on complex neural architectures and high-dimensional spaces.

Demystifying attention, the key mechanism inside transformers and LLMs. Grant delves into the inner workings of transformers in large language models, focusing on the attention mechanism and its data processing capabilities.

Grant provides an in-depth visual explanation of transformers in AI, discussing their structure, function, and the process of generating text using GPT models. Transformers use attention mechanisms and vast data to revolutionize text generation in AI.

A visual course on the fundamentals of LLMs including text representation, text generation, semantic search and prompt engineering.

A visualization and walkthrough of LLM algorithm that backs OpenAI’s ChatGPT. Explore the algorithm down to every add & multiply, seeing the whole process in action.

This workshop covers how to analyze and fix LLMs, finetuning with Unsloth and a deep dive into LLM technicals.

A practical course for building LLM-based applications with Charles Frye, Sergey Karayev, and Josh Tobin.

An introductory guide to LLMs captures the evolving ecosystem and practical application.

Summarizing recent developments and ethical concerns in the field of large language models.

Explore the fundamentals of prompt engineering for optimizing Large Language Model outputs.

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. This course is divided into three parts: 1. LLM Fundamentals covers essential knowledge about mathematics, Python, and neural networks. 2. The LLM Scientist focuses on building the best possible LLMs using the latest techniques. 3. The LLM Engineer focuses on creating LLM-based applications and deploying them. For an interactive version of this course, Maxime has created two LLM assistants that will answer questions and test your knowledge in a personalized way.

Understand and build LLMs from scratch in PyTorch.

Kaggle is a popular online platform for data science and machine learning competitions, as well as a community for data enthusiasts, professionals, and researchers. It provides a wide range of datasets, notebooks, and public code to help users learn and develop their skills in data science and machine learning.

Hugging Face is a leading open-source platform for machine learning (ML) and natural language processing (NLP) that provides a suite of tools, libraries, and models. It is best known for its Transformers library, which simplifies the implementation of state-of-the-art NLP models like GPT, BERT, and T5. Hugging Face offers easy access to pre-trained models for tasks such as text classification, text generation, translation, and more.

A 1 hour general-audience introduction to LLMs. Understanding and advancing large language models involves technical, ethical, and security considerations. Taught by Andrej Kaparthy, a founding member of OpenAI and a ex-senior director of AI at Tesla.