Inside Deep Learning: Math, Algorithms, Models (Paperback)

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.

《深度學習內幕》是為日常開發者而寫的，其中沒有複雜的數學證明或不必要的學術理論。

通過深入學習的理論和實踐，並應用創新技術來解決日常數據問題。《深度學習內幕》是一本快節奏的初學者指南，用於解決深度學習中常見的技術問題。

《深度學習內幕》是為日常開發者而寫的，其中沒有複雜的數學證明或不必要的學術理論。通過平實的語言、註釋代碼和方程式，您將學習深度學習的工作原理，並通過數十個即時有用的PyTorch示例進行實踐。在學習的過程中，您將構建一個與專業機器翻譯相同原理的法英翻譯器，並發現最新研究中剛出現的尖端技術。最重要的是，本書中的每個深度學習解決方案都可以在不到十五分鐘的時間內使用免費GPU硬件運行！

購買印刷版書籍將包括一本免費的PDF、Kindle和ePub格式的電子書，由Manning Publications提供。

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.

Edward Raff是Booz Allen Hamilton的首席科學家，也是JSAT機器學習庫的作者。他的研究領域包括深度學習、惡意軟體檢測、機器學習的可重複性、公平性/偏見以及高性能計算。他還是馬里蘭大學巴爾的摩縣分校的客座教授，並在該校的數據科學系教授深度學習。Raff博士已發表了40多篇同行評審的論文，獲得了三個最佳論文獎，並在許多重要的會議上發表演講。