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DataCamp

Hugging Face Fundamentals

via DataCamp

Overview

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Join the world's biggest AI community today, and learn to build AI with Hugging Face! In this skill track, built in collaboration with the Hugging Face team, you'll learn how to access and fine-tune with models and datasets from Hugging Face, and build AI agents with Hugging Face's smolagents framework.

You'll start by getting familiar with the Hugging Face Hub and the transformers library for downloading and performing inference with LLMs. You'll use these models for natural language tasks like summarization, classification, and document question-answering.

You'll also become familiar with fine-tuning these models for your own use cases, working with custom datasets to modify the model's parameters and improve performance.

You won't be limited to text applications either! You'll work with computer vision models, audio models, and even video generation models! You'll also get to connected some of these domains together to create multi-modal workflows like image-text to image and image-text to text.

Finally, AI agents are the talk of the town, and Hugging Face has their own AI agent framework: smolagents! smolagents is an easy-to-use framework for building coding agents, which perform actions by writing and executing code.

Start your journey as an AI builder today!

Syllabus

  • Working with Hugging Face
    • Navigate and use the extensive repository of models and datasets available on the Hugging Face Hub.
  • Introduction to LLMs in Python
    • Learn the nuts and bolts of LLMs and the revolutionary transformer architecture they are based on!
  • Multi-Modal Models with Hugging Face
    • Combine text, images, audio, and video with the latest AI models from Hugging Face, and generate new images and videos!
  • Food Image Classification with Hugging Face
  • AI Agents with Hugging Face smolagents
    • Learn how to build intelligent agents that reason, act, and solve real-world tasks using Python.

Taught by

Jasmin Ludolf, Iván Palomares Carrascosa, Jacob Marquez, Sean Benson, and Adel Nehme

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