Presentation: Jupyter Notebooks: Interactive Visualization Approaches
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Jupyter Notebooks are becoming the IDE of choice for data scientists and researchers. They provide the users with a nice exploratory environment where they can quickly research and prototype different models and visualize the results all in one place. Notebooks are easy to share and can be converted into documents/slides to present to stakeholders.
With widget libraries like ipywidgets and bqplot, users can create rich applications, dashboards and tools by just using python code.
In this talk, we will see how we can build interactive visualizations in the Jupyter notebook. In the first part of the talk, I'll introduce the widget libraries and walk you through the code of a simple example so we understand how to assemble and link these widgets. Then we'll look at usecases including building dashboards from server logs, twitter sentiment analysis and finally tools for building, training and diagnosing deep learning models.
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