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Product Details 基本信息 ISBN-13 书号 9781484251768 Author 作者 Sridhar Alla Format 版本 平装-胶订 Pages Number 页数 416页 Publisher 出版社 Apress Publication Date 出版日期 2019-10-11 Product Dimensions 商品尺寸 10 x 7 x 0.8 cm Shipping Weight 商品重量 1650g Language 语种 英语 Book Contents 内容简介 Utilize this easy-to-follow beginner’s guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks.This book begins with an explanation of what anomaly detection is, what it is used for, and its importance. After covering statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to deep learning with details on how
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Product Details 基本信息 ISBN-13 书号 9781789956177 Author 作者 Ivan Vasilev Format 版本 平装-胶订 Pages Number 页数 468页 Publisher 出版社 Packt Publishing Publication Date 出版日期 2019-12-19 Product Dimensions 商品尺寸 9.2 x 7.5 x 0.9 cm Shipping Weight 商品重量 1760g Language 语种 英语 Book Contents 内容简介 This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.
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Product Details 基本信息 ISBN-13 书号 9781789804744 Author 作者 Alex Galea, Luis Capelo Format 版本 平装-胶订 Pages Number 页数 334页 Publication Date 出版日期 2018-08-31 Language 语种 英语 Book Contents 内容简介 If you’re a Python programmer stepping into the world of data science, this is the ideal way to get started.
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Product Details 基本信息 ISBN-13 书号 9781492041412 Author 作者 Weidman Format 版本 平装-胶订 Pages Number 页数 252页 Publisher 出版社 O’Reilly Media Publication Date 出版日期 1800-01-01 Product Dimensions 商品尺寸 9.1 x 7 x 0.5 cm Shipping Weight 商品重量 900g Language 语种 英语
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Product Details 基本信息 ISBN-13 书号 9781484235157 Author 作者 Manaswi Format 版本 平装-胶订 Pages Number 页数 219页 Publication Date 出版日期 2018-05-18 Language 语种 英语 Book Contents 内容简介 Build deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning.This book covers intermediate and advanced levels of deep learning, including convolutional neural networks, recurrent neural
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Product Details 基本信息 ISBN-13 书号 9781617296079 Author 作者 Duerr Oliver Format 版本 平装-胶订 Pages Number 页数 296页 Publisher 出版社 Manning Publications Publication Date 出版日期 1800-01-01 Language 语种 英语
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Product Details 基本信息 ISBN-13 书号 9781260462296 Author 作者 Reddy Konasani Venkata Format 版本 精装 Pages Number 页数 560页 Publication Date 出版日期 2021-02-11 Product Dimensions 商品尺寸 10.10 x 8.10 x 1.30 Shipping Weight 商品重量 2.60磅 Language 语种 其它(含多语)
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Product Details 基本信息 ISBN-13 书号 9781484268087 Author 作者 Sanghi Format 版本 平装-胶订 Pages Number 页数 382页 Publisher 出版社 Apress Publication Date 出版日期 2021-04-14 Language 语种 其它(含多语) Book Contents 内容简介 Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics, healthcare, finance, and many more. This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding exercise.You’ll begin by reviewing the Markov decision processes, Bellman equations, and dynamic programming that form the core concepts and foundation of deep reinforcement learning. Next, you’ll study model-free learning followed by function approximation using neural networks and deep learning. This is followed by various deep reinforcement learning algorithms such as deep q-networks, various
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