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By Yin Chen, Rich Hagarty, Catherine Diep, Simeon Monov, Lukasz Cmielowski, Mark Sturdevant | Published November 19, 2018 - Updated November 19, 2018
AnalyticsArtificial IntelligenceData ScienceMachine LearningObject StoragePython
Recognizing handwritten numbers is a piece of cake for humans, but it’s a non-trivial task for machines. Currently, however, with the advancement of machine learning, people have made machines more capable of performing this task. We now have mobile banking apps that can scan checks in seconds, and accounting software that can extract dollar amounts from thousands of contracts in minutes. If you are interested in knowing how this all works, please follow along with this code pattern as we take you through the steps to create a simple handwritten digit recognizer in Watson Studio and PyTorch.
In this code pattern, you’ll use Jupyter Notebook in IBM Watson Studio to access pre-installed and optimized PyTorch environments through Python client library of Watson Machine Learning Service. The library has a set of REST APIs in its core that allow you to submit training jobs, monitor status, store, and deploy models.
When you have completed this code pattern, you’ll understand how to:
Get the detailed instructions in the README file. These steps will show you how to:
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