Applying Hugging Face Machine Learning Pipelines in Python
Gain insights into Hugging Face’s AI models for NLP and computer vision. Explore transformer-based pipelines, apply them for tasks like classification and object detection, using Python and PyTorch.
4.7
17 Lessons
40min
Updated 2 months ago
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Hugging Face is a community-driven effort to develop and promote artificial intelligence for a wide array of applications. The organization’s pre-trained, state-of-the-art deep learning models can be deployed to various machine learning tasks.
In this course, you’ll explore the Hugging Face artificial intelligence library with particular attention to natural language processing (NLP) and computer vision. You’ll first explore Hugging Face’s approach to deep learning with specific attention to transformers. You’ll then learn Hugging Face’s pipeline API model and apply various pipelines to unique NLP tasks such as classification, summarization, question answering, and more. You’ll continue with a new set of Hugging Face pipelines for computer vision tasks including object detection and segmentation.
By the end of this course, you’ll be familiar with a wide array of Hugging Face’s pipelines for common machine learning tasks and their implementation in Python using pytorch.
Hugging Face is a community-driven effort to develop and promote artificial intelligence for a wide array of applications. The o...Show More
WHAT YOU'LL LEARN
A familiarity with Hugging Face and their library of machine learning models
A working knowledge of Hugging Face’s pipeline APIs and their applications
The ability to apply Hugging Face models to generate and read text using natural language processing
The ability to apply Hugging Face models to computer vision tasks
Hands-on experience implementing Hugging Face models using Python and PyTorch
A familiarity with Hugging Face and their library of machine learning models
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TAKEAWAY SKILLS
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