Overview
Navigate and use the extensive repository of models and datasets available on the Hugging Face Hub. In today's rapidly evolving landscape of machine learning (ML) and artificial intelligence (AI), Hugging Face stands out as a vital platform, allowing anyone to leverage the latest advancements in their projects. To begin, you'll navigate the Hugging Face Hub's vast model and dataset repository. You'll also discover the power of Large Language Models and Transformers, exploring the diverse range available. You'll discover how the models and datasets can be applied to tasks ranging from sentiment analysis to language translation. Furthermore, we'll extend our exploration to image and audio processing. Pipelines are the backbone of many ML and AI workflows. You'll start with the basics of the pipeline module and Auto classes from the transformers library. Then, you'll build pipelines for natural language processing tasks before moving on to image and audio processing, ensuring you have the tools to tackle a wide range of tasks efficiently. Finally, you'll dive into different frameworks for fine-tuning, text generation, and embeddings. You'll go through a fine-tuning example before exploring the concept of embeddings in machine learning, understanding how they capture semantic information. By the end of the course, you'll be equipped with the knowledge and skills to tackle a wide range of ML and AI tasks effectively using the Hugging Face Hub.
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