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Deep Learning with TensorFlow – Second Edition


Deep Learning with TensorFlow – Second Edition by Giancarlo Zaccone
English | 29 Mar. 2018 | ISBN: 1788831101 | 484 Pages | EPUB | 17.2 MB

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guideKey FeaturesLearn how to implement advanced techniques in deep learning with Google’s brainchild, TensorFlowExplore deep neural networks and layers of data abstraction with the help of this comprehensive guideReal-world contextualization through some deep learning problems concerning research and applicationBook DescriptionDeep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data and has been fully updated to the latest version of TensorFlow 1.x.Throughout the…and progress of deep learningLearn about deep machine intelligence and GPU computing with the latest TensorFlow 1.xAccess public datasets and utilize them using TensorFlow to load, process, and transform dataUse TensorFlow on real-world datasets, including images, text, and moreLearn how to evaluate the performance of your deep learning modelsUsing deep learning for scalable object detection and mobile computingTrain machines quickly to learn from data by exploring reinforcement learning techniquesExplore active areas of deep learning research and applicationsWho This Book Is ForThe book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.

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