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Posted by James Wexler, Senior Software Engineer, Google Big Picture Team (Cross-posted on the Google Open Source Blog) Getting the best results out of a machine learning (ML) model requires that you truly understand your data. However, ML datasets can contain hundreds of millions of data points, each consisting of hundreds (or even thousands) of features, making it nearly impossible to understand
Posted by Jonathan Huang, Research Scientist and Vivek Rathod, Software Engineer (Cross-posted on the Google Open Source Blog) At Google, we develop flexible state-of-the-art machine learning (ML) systems for computer vision that not only can be used to improve our products and services, but also spur progress in the research community. Creating accurate ML models capable of localizing and identif
Posted by Andrew G. Howard, Senior Software Engineer and Menglong Zhu, Software Engineer (Cross-posted on the Google Open Source Blog) Deep learning has fueled tremendous progress in the field of computer vision in recent years, with neural networks repeatedly pushing the frontier of visual recognition technology. While many of those technologies such as object, landmark, logo and text recognition
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