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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 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:
In this liveProject, you’ll master topic modeling—an amazing skill for quickly analyzing textual datasets. You’ll learn to apply the cutting-edge neural topic model in a real-world setting:
geekle is based on a wordle clone.