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Book is in Used-Good condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain limited notes and highlighting. 1.62

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《深度学习》由全球知名的三位专家Ian Goodfellow、Yoshua Bengio 和Aaron Courville撰写，是深度学习领域奠基性的经典教材。全书的内容包括3个部分：第1部分介绍基本的数学工具和机器学习的概念，它们是深度学习的预备知识；第2部分系统深入地讲解现今已成熟的深度学习方法和技术；第3部分讨论某些具有前瞻性的方向和想法，它们被公认为是深度学习未来的研究重点。《深度学习》适合各类读者阅读，包括相关专业的大学生或研究生，以及不具有机器学习或统计背景、但是想要快速补充深度学习知识，以便在实际产品或平台中应用的软件工程师。

Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context. Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively. In this insightful book, NLP expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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Book is in Used-Good condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain limited notes and highlighting. 1.05

Book is in Used-Good condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain limited notes and highlighting. 1.11

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Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you, and your deep learning skills, become more sophisticated. Deep Learning with PyTorch teaches you how to implement deep learning algorithms with Python and PyTorch. This book takes you into a fascinating case study: building an algorithm capable of detecting malignant lung tumors using CT scans. As the authors guide you through this real example, you'll discover just how effective and fun PyTorch can be. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.Dive into machine learning concepts in general, as well as deep learning in particularUnderstand how deep networks evolved from neural network fundamentalsExplore the major deep network architectures, including Convolutional and RecurrentLearn how to map specific deep networks to the right problemWalk through the fundamentals of tuning general neural networks and specific deep network architecturesUse vectorization techniques for different data types with DataVec, DL4J’s workflow toolLearn how to use DL4J natively on Spark and Hadoop

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