Neural Topic Models you own this product

prerequisites
intermediate Python • linear algebra • basics of deep learning
skills learned
exploring the vector representations produced by BERT • preprocessing text documents • using the Contextual Topic Model • performing hyperparameter tuning • evaluating topics using Coherence and Diversity and visualizing generated topics
Aneesha Bakharia
1 week · 6-8 hours per week · INTERMEDIATE
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In this liveProject, you’ll use the neural network-inspired Contextual Topic Model to identify and visualize all of the articles in a scientific magazine’s back catalog. This cutting-edge technique is made easy by the OCTIS (Optimizing and Comparing Topic Models is Simple!) library. Once you’ve established your text-processing pipeline, you’ll use coherence and diversity metrics to evaluate the output of your topic models, tune your neural network’s hyperparameters to improve results, and visualize your results for printing on posters and other media.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Aneesha Bakharia
Aneesha Bakharia completed her PhD in interactive topic modeling at Queensland University of Technology in Australia. She is currently the Manager of Learning Analytics at The University of Queensland where she leads a team of programmers and data scientists. She has written 10 books on programming and web development for Cengage publishing. She also blogs about data science related topics, including topic modeling and she publishes academic peer reviewed publications on educational technologies and learning analytics.

prerequisites

This liveProject is for data scientists and developers who are confident programming with Python and the Python data ecosystem. To begin this liveProject you will need to be familiar with the following:


TOOLS
  • Intermediate Python
  • Basics of Jupyter Notebook
TECHNIQUES
  • Linear algebra
  • Basics of deep learning

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