Build, debug, and optimize AI agents using DSPy and MLflow.

Anthropic's Interactive Prompt Engineering Tutorial - anthropics/prompt-eng-interactive-tutorial

Reinforcement Learning has helped train neural networks to win games, drive cars and even get ChatGPT to sound more human when it responds to your prompt. Th...

Shortform link: https://shortform.com/artem 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 Introduction 01:28 Historical background 02:50 Curve Fitting problem 06:26 Random vs guided adjustments 09:43 Derivatives 14:34 Gradient Descent 16:23 Higher dimensions 21:36 Chain Rule Intuition 27:01 Computational Graph and Autodiff 36:24 Summary 38:16 Shortform 39:20 Outro USEFUL RESOURCES: Andrej Karpathy's playlist: https://youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ&si=zBUZW5kufVPLVy9E Jürgen Schmidhuber's blog on the history of backprop: https://people.idsia.ch/~juergen/who-invented-backpropagation.html CREDITS: Icons by https://www.freepik.com/

A library for graph deep learning research. Contribute to divelab/DIG development by creating an account on GitHub.

Colab 01. Learning LangGraph Agent Executor: https://drp.li/vL1J9 Colab 02. Learning LangGraph - Chat Executor: https://drp.li/HAz3o Colab 03. Learning LangGraph - Agent Supervisor: https://drp.li/xvEwd Interested in building LLM Agents? Fill out the form below Building LLM Agents Form: https://drp.li/dIMes Github: https://github.com/samwit/langchain-tutorials (updated) https://github.com/samwit/llm-tutorials Time Stamps: 00:00 Intro 00:19 What is LangGraph? 00:26 LangGraph Blog 01:38 StateGraph 02:16 Nodes 02:42 Edges 03:48 Compiling the Graph 05:23 Code Time 05:34 Agent with new create_open_ai 21:37 Chat Executor 27:00 Agent Supervisor

Discussions: Hacker News (65 points, 4 comments), Reddit r/MachineLearning (29 points, 3 comments) Translations: Arabic, Chinese (Simplified) 1, Chinese (Simplified) 2, French 1, French 2, Italian, Japanese, Korean, Persian, Russian, Spanish 1, Spanish 2, Vietnamese Watch: MIT’s Deep Learning State of the Art lecture referencing this post Featured in courses at Stanford, Harvard, MIT, Princeton, CMU and others In the previous post, we looked at Attention – a ubiquitous method in modern deep learning models. Attention is a concept that helped improve the performance of neural machine translation applications. In this post, we will look at The Transformer – a model that uses attention to boost the speed with which these models can be trained. The Transformer outperforms the Google Neural Machine Translation model in specific tasks. The biggest benefit, however, comes from how The Transformer lends itself to parallelization. It is in fact Google Cloud’s recommendation to use The Transformer as a reference model to use their Cloud TPU offering. So let’s try to break the model apart and look at how it functions. The Transformer was proposed in the paper Attention is All You Need. A TensorFlow implementation of it is available as a part of the Tensor2Tensor package. Harvard’s NLP group created a guide annotating the paper with PyTorch implementation. In this post, we will attempt to oversimplify things a bit and introduce the concepts one by one to hopefully make it easier to understand to people without in-depth knowledge of the subject matter. 2020 Update: I’ve created a “Narrated Transformer” video which is a gentler approach to the topic: A High-Level Look Let’s begin by looking at the model as a single black box. In a machine translation application, it would take a sentence in one language, and output its translation in another.