Inside Deep Learning: Math, Algorithms, Models

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Inside Deep Learning: Math, Algorithms, Models

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內容描述

Written for everyday developers, there are no complex mathematical proofs or unnecessary academic theory in Inside Deep Learning. Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. Inside Deep Learning is a fast-paced beginner's guide to solving common technical problems with deep learning. Written for everyday developers, there are no complex mathematical proofs or unnecessary academic theory in Inside Deep Learning. You'll learn how deep learning works through plain language, annotated code, and equations as you work through dozens of instantly useful PyTorch examples. As you go, you'll build a French-English translator that works on the same principles as professional machine translation, and discover cutting-edge techniques just emerging from the latest research. Best of all, every deep learning solution in this book can run in less than fifteen minutes using free GPU hardware! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

作者介紹

Edward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. His research includes deep learning, malware detection, reproducibility in ML, fairness/bias, and high performance computing. He is also a visiting professor at the University of Maryland, Baltimore County and teaches deep learning in the Data Science department. Dr Raff has over 40 peer reviewed publications, three best paper awards, and has presented at numerous major conferences.