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.

Applying Hugging Face Machine Learning Pipelines in Python.png

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JuPyter

JuPy-Translation

JuPy-QA

JuPy-Text-Classification

JuPy-Summarization

JuPy-Text-Generation

JuPy-Similarity

JuPy-Fill-Mask

JuPy-Image-Classification

JuPy-Object-Detection

JuPy-Segmentation

JuPy-Image-to-Text

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.

Applying Hugging Face Machine Learning Pipelines in Python

## Inference
**#key# Sequence-to-sequence: The NLP task in which both inputs and outputs are sequences rather than scalars. #key#** models have taken some NLP tasks to a new level. Text summarization isn't an exception either, because we can use models like Distilbart, BART, Pegasus, and many others.




Learn to use Hugging Face models for text summarization.

Text Summarization

Introduction

NLP

Computer Vision

Conclusion

Appendix

Text Summarization

Overview

Inference