Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer
Learn how to implement RAG (Retrieval Augmented Generation) from scratch, straight from a LangChain software engineer. This Python course teaches you how to use RAG to combine your own custom data with the power of Large Language Models (LLMs). 💻 Code: https://github.com/langchain-ai/rag-from-scratch ⭐️ Course Contents ⭐️⌨️ (0:00:00) Overview⌨️ (0:05:53) Indexing⌨️ (0:10:40) Retrieval⌨️ (0:15:52) Generation⌨️ (0:22:14)…
Attention in transformers, visually explained | DL6
Demystifying attention, the key mechanism inside transformers and LLMs.
Stanford CS236: Deep Generative Models I 2023 I Lecture 1 – Introduction
To follow along with the course, visit the course website:https://deepgenerativemodels.github.io/ Stefano ErmonAssociate Professor of Computer Science, Stanford Universityhttps://cs.stanford.edu/~ermon/ https://www.youtube.com/watch?v=XZ0PMRWXBEU
Fine-tuning Large Language Models (LLMs) | w/ Example Code
This is the 5th video in a series on using large language models (LLMs) in practice. Here, I discuss how to fine-tune an existing LLM for a particular use case and walk through a concrete example with Python code.
Prompt Engineering Tutorial – Master ChatGPT and LLM Responses
Learn prompt engineering techniques to get better results from ChatGPT and other LLMs.
The math behind Attention: Keys, Queries, and Values matrices
This is the second of a series of 3 videos where we demystify Transformer models and explain them with visuals and friendly examples. 00:00 Introduction01:18 Recap: Embeddings and Context04:46 Similarity11:09 Attention20:46 The Keys and Queries Matrices25:02 The Values Matrix28:41 Self and Multi-head attention33:54: Conclusion
The Attention Mechanism in Large Language Models
Attention mechanisms are crucial to the huge boom LLMs have recently had.In this video you’ll see a friendly pictorial explanation of how attention mechanisms work in Large Language Models.This is the first of a series of three videos on Transformer models. https://www.youtube.com/watch?v=OxCpWwDCDFQ
What are Transformer Models and how do they work?
This is the last of a series of 3 videos where we demystify Transformer models and explain them with visuals and friendly examples. 00:00 Introduction01:50 What is a transformer?04:35 Generating one word at a time08:59 Sentiment Analysis13:05 Neural Networks18:18 Tokenization19:12 Embeddings25:06 Positional encoding27:54 Attention32:29 Softmax35:48 Architecture of a Transformer39:00 Fine-tuning42:20 Conclusion
Stanford CS229 Machine Learning I Introduction I 2022 I Lecture 1
For more information about Stanford’s Artificial Intelligence programs visit: https://stanford.io/ai https://www.youtube.com/watch?v=Bl4Feh_Mjvo To follow along with the course, visit:https://cs229.stanford.edu/syllabus-s… Tengyu MaAssistant Professor of Computer Sciencehttps://ai.stanford.edu/~tengyuma/ Christopher RéAssociate Professor of Computer Sciencehttps://cs.stanford.edu/~chrismre/
