Neural language models, and an explanation of recurrent neural networks
Chapters
0:00 Introduction
1:54 Neural N-Gram Models
6:03 Recurrent Neural Networks
11:47 LSTM Cells
12:22 Outro
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…
The attention mechanism is well known for its use in Transformers. But where does it come from? It’s origins lie in fixing a strange problems of RNNs. Chapters0:00 Introduction0:22 Machine Translation2:01 Attention Mechanism8:04 Outro
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…
In this video we will talk about backpropagation – an algorithm powering the entire field of machine learning and try to derive it from first principles. OUTLINE:00:00 Introduction01:28 Historical background02:50 Curve Fitting problem06:26 Random vs guided adjustments09:43 Derivatives14:34 Gradient Descent16:23 Higher dimensions21:36 Chain Rule Intuition27:01 Computational Graph and Autodiff36:24 Summary38:16 Shortform39:20 Outro Jürgen Schmidhuber’s blog…