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by Rich Hagarty, Scott DAngelo, Franck Barillaud | Updated May 16, 2017 - Published May 15, 2017
AnalyticsArtificial intelligenceData scienceDeep learningMachine learningSystems
This code pattern teaches developers to quickly train a machine learning algorithm using PowerAI virtualization software through Nimbix. You can increase speeds over a non-Power architecture when running unsupervised learning iterations using NVIDIA GPUs and the CUDA parallel computing platform.
This code pattern is designed for anyone who wants to increase their machine learning speed, showing you how to leverage IBM’s new PowerAI for machine learning. You’ll use a Jupyter Notebook to showcase an example of machine learning with a time series on IBM Power8® systems. The notebook focuses on evaluating the predictability of future financial market values in the renewable energy sector by examining related markets and sentiment detected in The New York Times news articles.
When you’ve completed this pattern, you will understand how to:
This pattern will assist application developers who need to efficiently build powerful deep learning applications and improve their machine learning speeds quickly. It’s also ideal for developers who do not have extensive data science experience.
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