Yann LeCun: Towards Machines That Can Understand, Reason, & Plan

Generative AI Agents represent the current frontier of LLM technology, enabling dynamic interactions and intelligent workflow automation. However, the complexities of architecting and deploying these agents can be daunting. In this live session, Patrick Marlow demystifies the process, guiding you through the critical decisions and trade-offs involved in building production-ready agents. Explore the full spectrum…

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)…

This is CS50, Harvard University’s introduction to the intellectual enterprises of computer science and the art of programming. TABLE OF CONTENTS 00:00:00 – Welcome00:01:01 – Introduction00:03:13 – Image Generation00:08:23 – ChatGPT00:11:06 – Prompt Engineering00:12:40 – CS50.ai00:19:03 – Generative AI00:22:08 – Decision Trees00:26:33 – Minimax00:34:27 – Machine Learning00:42:56 – Deep Learning00:48:53 – Large Language Models00:53:36 –…

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

LangChain became immensely popular when it was launched in 2022, but how can it impact your development and application of AI models, Large Language Models (LLM) in particular. In this video Martin Keen shares an overview of the features and uses of LangChain.