Track
Hugging Face Fundamentals
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Additional Learning Available
Additional Hugging Face training can be found on the official Hugging Face website!
Описание трека
Hugging Face Fundamentals
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!
Предварительные требования
Для участия в этом курсе никаких предварительных требований нет.Course
Navigate and use the extensive repository of models and datasets available on the Hugging Face Hub.
Course
Learn the nuts and bolts of LLMs and the revolutionary transformer architecture they are based on!
Course
Combine text, images, audio, and video with the latest AI models from Hugging Face, and generate new images and videos!
Project
Classify food images using Hugging Face models, automatically recognizing and categorizing food items.
Course
Learn how to build intelligent agents that reason, act, and solve real-world tasks using Python.
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