Summarize COVID-19 News Using NLP and PyTorch

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.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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:

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

you will learn

In this liveProject, you’ll master PyTorch-based text summarization, a useful and easily transferable data science task. Text summarization can be applied to many real-world applications such as legal document summarization, classified document analysis, spam detection, and more.

  • Sequence to sequence models with attention techniques
  • Text data cleanup techniques
  • Feature generation techniques
  • Model building using PyTorch
  • Model debugging with TensorBoard
  • Analyzing model performance tradeoffs

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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