Learn to build AI agents with long-term memory with LangGraph, using LangMem for memory management.

Learn the fundamentals of prompt engineering for ChatGPT. Learn effective prompting, and how to use LLMs for summarizing, inferring, transforming, and expanding.

Learn and build diffusion models from the ground up, understanding each step. Learn about diffusion models in use today and implement algorithms to speed up sampling.

Learn to break down complex tasks, automate workflows, chain LLM calls, and get better outputs from LLMs. Evaluate LLM inputs and outputs for safety and relevance.

Create and demo machine learning applications quickly. Share your app with teammates and beta testers on Hugging Face Spaces.

Learn Microsoft's open source orchestrator, Semantic Kernel and use LLM building blocks such as memory, connectors, chains and planners in your apps.

Learn how to accelerate the application development process with text embeddings for sentence and paragraph meaning.

Learn how to prompt an LLM to help improve, debug, understand, and document your code. Use LLMs to simplify your code and enhance productivity.

Design and execute real-world applications of vector databases. Build efficient, practical applications, including hybrid and multilingual searches.

Learn how to evaluate the safety and security of your LLM applications and protect against risks. Monitor and enhance security measures to safeguard your apps.

Learn advanced RAG retrieval methods like sentence-window and auto-merging that outperform baselines, and evaluate and iterate on your pipeline's performance.

Learn advanced retrieval techniques to improve the relevancy of retrieved results. Learn to recognize poor query results and use LLMs to improve queries.

Expand your toolkit with LangChain.js, a JavaScript framework for building with LLMs. Understand the fundamentals of using LangChain to orchestrate and chain modules.

Learn how to deploy an LLM-based application into production using serverless technology. Learn to prompt and customize LLM responses with Amazon Bedrock.

Learn best practices for prompting and selecting among Meta Llama 2 & 3 models. Interact with Meta Llama 2 Chat, Code Llama, and Llama Guard models.

Learn how to build and use knowledge graph systems to improve your retrieval augmented generation applications. Use Neo4j's query language Cypher to manage and retrieve data.

Build a full-stack web application that uses RAG capabilities to chat with your data. Learn to build a RAG application in JavaScript, using an intelligent agent to answer queries.

Improve your RAG system to retrieve diverse data types. Learn to extract and normalize content from a wide variety of document types, such as PDFs, PowerPoints, and HTML files.

Build autonomous agents that intelligently navigate and analyze your data. Learn to develop agentic RAG systems using LlamaIndex, enabling powerful document Q&A and summarization. Gain valuable skills in guiding agent reasoning and debugging.

Automate business workflows with multi-AI agent systems. Exceed the performance of prompting a single LLM by designing and prompting a team of AI agents through natural language.

Use the AutoGen framework to build multi-agent systems with diverse roles and capabilities for implementing complex AI applications.

Build agentic AI workflows using LangChain's LangGraph and Tavily's agentic search.

Train your machine learning models using cleaner energy sources.

Learn a flexible framework to build a variety of complex AI applications.

Try out the features of the new Llama 3.2 models to build AI applications with multimodality.

Efficiently handle time-varying workloads with serverless agentic workflows and responsible agents built on Amazon Bedrock.

Build agents that collaborate to solve complex business tasks.

Move your LLM-powered applications beyond proof-of-concept and into production with the added control of guardrails.

Learn to build with LLMs by creating a fun interactive game from scratch.

Learn to use OpenAI Canvas to write, code, and create more effectively in collaboration with AI.

Learn how to use and prompt OpenAI's o1 model for complex reasoning tasks.

Build LLM apps that can process very long documents using the Jamba model

Learn how an AI Assistant is built to use and accomplish tasks on computers.

Understand the transformer architecture that powers LLMs to use them more effectively.

Understand and implement the attention mechanism, a key element of transformer-based LLMs, using PyTorch.

Learn how to systematically evaluate, improve, and iterate on AI agents using structured assessments.

Learn to build, debug, and deploy applications with an Agentic AI-powered integrated development environment.

Build an event-driven agentic workflow to process documents and fill forms using RAG and human-in-the-loop feedback.