Huggingface Course Deepwiki

This document provides a technical overview of the Hugging Face Course repository, its structure, and how it operates. The course is designed to teach students about the Hugging Face ecosystem, with a focus on large language models (LLMs), natural language processing (NLP), and how to use libraries like 🤗 Transformers, 🤗 Datasets,... The Hugging Face Course is an open-source educational resource focused on teaching students how to apply Transformer models to various tasks in natural language processing and beyond. The course is completely free and available in multiple languages. The repository at https://github.com/huggingface/course contains all the content used to build the course website at https://huggingface.co/course. It includes text explanations, code snippets, diagrams, and utilities for generating Jupyter notebooks from the course content.

Sources: README.md1-4 chapters/en/chapter1/1.mdx10-14 The course repository is organized into several main directories: This document provides a comprehensive technical overview of the Hugging Face AI Agents Course repository, which serves as an educational platform for teaching AI agent development from fundamentals to advanced applications. The repository contains a complete curriculum covering agent theory, practical frameworks, and real-world implementations. For specific information about individual agent frameworks, see smolagents Framework, LlamaIndex Framework, and LangGraph Framework. For details about practical applications, see Use Cases and Applications.

For information about the course delivery infrastructure, see Course Platform and Infrastructure. The course is organized as a progressive learning journey, structured through a hierarchical table of contents system that guides students from basic concepts to advanced implementations. The course follows a four-unit progression with additional bonus content. Each unit builds upon previous concepts while introducing new frameworks and applications. Sources: units/en/_toctree.yml1-173 units/en/unit0/introduction.mdx56-72 README.md8-28 A Go implementation of the Model Context Protocol (MCP), enabling seamless integration between LLM applications and external data sources and tools.

Fully local web research and report writing assistant Utilities intended for use with Llama models. 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. 🦜🔗 Build context-aware reasoning applications and get access to the augmented documentation experience In the last unit, we learned our first reinforcement learning algorithm: Q-Learning, implemented it from scratch, and trained it in two environments, FrozenLake-v1 ☃️ and Taxi-v3 🚕.

We got excellent results with this simple algorithm, but these environments were relatively simple because the state space was discrete and small (16 different states for FrozenLake-v1 and 500 for Taxi-v3). For comparison, the state space in Atari games can contain10910^{9}109 to101110^{11}1011 states. But as we’ll see, producing and updating a Q-table can become ineffective in large state space environments. So in this unit, we’ll study our first Deep Reinforcement Learning agent: Deep Q-Learning. Instead of using a Q-table, Deep Q-Learning uses a Neural Network that takes a state and approximates Q-values for each action based on that state. This repo contains the content that's used to create the Hugging Face course.

The course teaches you about applying Transformers to various tasks in natural language processing and beyond. Along the way, you'll learn how to use the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub. It's completely free and open-source! As part of our mission to democratise machine learning, we'd love to have the course available in many more languages! Please follow the steps below if you'd like to help translate the course into your language 🙏. To get started, navigate to the Issues page of this repo and check if anyone else has opened an issue for your language.

If not, open a new issue by selecting the Translation template from the New issue button. Once an issue is created, post a comment to indicate which chapters you'd like to work on and we'll add your name to the list. Since it can be difficult to discuss translation details quickly over GitHub issues, we have created dedicated channels for each language on our Discord server. If you'd like to join, follow the instructions at this channel 👉: https://discord.gg/JfAtkvEtRb This document covers the educational initiatives, courses, and learning materials provided through the Hugging Face blog ecosystem. The educational resources span from comprehensive multi-unit courses to standalone tutorials, targeting audiences from beginners to researchers.

For information about community events and workshops, see Community and Events. For technical training guides and optimization techniques, see Model Training and Optimization. The educational content system is built around structured learning paths, practical implementations, and community-driven knowledge sharing. Sources: deep-rl-intro.md, deep-rl-q-part1.md, deep-rl-q-part2.md, deep-rl-dqn.md, deep-rl-pg.md, deep-rl-a2c.md, deep-rl-ppo.md Educational Initiatives and Target Audiences and get access to the augmented documentation experience

This course will teach you about large language models (LLMs) and natural language processing (NLP) using libraries from the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as... We’ll also cover libraries outside the Hugging Face ecosystem. These are amazing contributions to the AI community and incredibly useful tools. While this course was originally focused on NLP (Natural Language Processing), it has evolved to emphasize Large Language Models (LLMs), which represent the latest advancement in the field. Throughout this course, you’ll learn about both traditional NLP concepts and cutting-edge LLM techniques, as understanding the foundations of NLP is crucial for working effectively with LLMs.

People Also Search

• huggingface/course | DeepWiki
• Hugging Face - Learn
• huggingface/agents-course | DeepWiki
• DeepWiki | AI documentation you can talk to, for every repo
• Deep Q-Learning - Hugging Face Deep RL Course
• neonwatty/course: The Hugging Face course on Transformers - GitHub
• huggingface - DeepWiki
• Complete Generative AI Course With Langchain and Huggingface
• Educational Resources | huggingface/blog | DeepWiki
• Introduction - Hugging Face LLM Course