Neural language models, and an explanation of recurrent neural networks

This video on the Artificial Intelligence tutorial will make you learn in detail about the different concepts involved in AI. You will understand the basics of AI and get an idea about Machine Learning and Deep Learning with hands-on demo in this Artificial Intelligence full course. You will look at how to become an AI…

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)…

Topics: Overview of course, OptimizationPercy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford Universityhttp://onlinehub.stanford.edu/ Associate Professor Percy LiangAssociate Professor of Computer Science and Statistics (courtesy) Assistant Professor Dorsa SadighAssistant Professor in the Computer Science Department & Electrical Engineering Department To follow along with the course schedule and syllabus, visit:https://stanford-cs221.github.io/autumn2019/#schedule artificialintelligencecourse 0:00 Introduction3:30 Why…

An introduction to language modeling, followed by an explanation of the N-Gram language model! Sources (includes the entire series): https://docs.google.com/document/d/1e… Chapters0:00 Introduction1:39 What is NLP?2:45 What is a Language Model?4:38 N-Gram Language Model7:20 Inference9:18 Outro

Aug 28, 2024Jürgen Schmidhuber, the father of generative AI shares his groundbreaking work in deep learning and artificial intelligence. In this exclusive interview, he discusses the history of AI, some of his contributions to the field, and his vision for the future of intelligent machines. Schmidhuber offers unique insights into the exponential growth of technology…

LLaMA3.2 has released a new set of compact models designed for on-device use cases, such as locally running assistants. Here, we show how LangGraph can enable these types of local assistant by building a multi-step RAG agent – this combines ideas from 3 advanced RAG papers (Adaptive RAG, Corrective RAG, and Self-RAG) into a single…