Summarize COVID-19 News Using NLP and PyTorch

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prerequisites
intermediate Python • beginner PyTorch • basics of deep learning • basics of natural language processing • basics of sequence-to-sequence modeling
skills learned
deep learning for NLP • model visualization with TensorBoard • sequence-to-sequence modeling • text summarization with attention techniques
Nahid Alam
5 weeks · 5-10 hours per week · INTERMEDIATE
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Text summarization is a powerful data science technique. It allows vital high-level information to be automatically extracted from reams of text data, without any slow and expensive human analysis.

In this liveProject, you’ll master text summarization techniques for summarizing news data by building a tool that can extract key information about COVID-19 from news articles using the PyTorch deep learning library. Your challenges will include cleaning your data set, building an attention-based deep learning model, and evaluating the success of your model using TensorBoard and ROUGE scores.

This project is designed for learning purposes and is not a complete, production-ready application or solution.
liveProject mentor Richard Dze shares what he likes about the Manning liveProject platform.

project author

Nahid Alam

Nahid Alam is a Machine Learning Engineer based out of San Francisco, with over four years experience building Machine Learning applications and AutoML systems that can train and build Machine Learning models in a distributed way. Nahid has a wide range of experience working at startups to fortune 500 companies, Venture Capital and founding startups. She has a Masters Degree in Computer Science from Clemson University.

prerequisites

The liveProject is for intermediate Python programmers who know the basics of data science. To begin this liveProject, you will need to be familiar with the following:

TOOLS
  • Intermediate Python
  • Basics of pandas
  • Basics of NumPy
  • Basics of scikit-learn
  • Basics of Jupyter Notebook
  • Basics of PyTorch
TECHNIQUES
  • Basics of machine learning
  • Basics of sequence-to-sequence modeling
  • Basics of deep learning concepts

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