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Visualizing your data using Python and validating computer vision deep learning models
Built for anyone that uses data to create Jupyter Notebooks and other artifacts, a pattern showcased this month shows the power of open source libraries like pandas, PixieDust, and folium. You'll also get great information around AI and gaming as well as learning how to create better models and predictions using data preprocessing.
Introducing solution starter kits: Perfect for the 2019 Coding Challenge! IBM recently teamed up with the United Nations Human Rights Office to host the first "solution starter" hackathon. Developers from some of the world's most influential companies teamed with business minds and subject matter experts in Switzerland at Call for Code Geneva to address how we might create solutions for mitigating the impact of natural disasters.
The result of this brainstorming event is four solution starter kits. Each kit provides you with a complete overview of the challenge, data sets, implementation technology, and a ton of support for you to bring these big ideas to life. To get a closer look at how we came up with the starter kits, take a look at Willie Tejada's latest post.
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Until next month,
Jill Amaya - Managing Editor, IBM Developer Artificial Intelligence