
Deep Learning with TensorFlowExplore neural networks and build intelligent systems with Python, 2nd EditionBy: Giancarlo Zaccone, Md. Rezaul Karim Publication Date: 20180330 Number of pages: 484 ISBN10: 1788831101 ISBN13: 9781788831109 Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow. Key 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 guideGain realworld contextualization through some deep learning problems concerning research and applicationBook DescriptionDeep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you'll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an indepth knowledge of machine learning techniques and the skills to apply them to realworld projects. What you will learnApply deep machine intelligence and GPU computing with TensorFlowAccess public datasets and use TensorFlow to load, process, and transform the dataDiscover how to use the highlevel TensorFlow API to build more powerful applicationsUse 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 for 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. Table of ContentsGetting Started with Deep LearningA First Look at TensorFlowFeedForward Neural Networks with TensorFlowConvolutional Neural NetworksOptimizing TensorFlow AutoencodersRecurrent Neural NetworksHeterogeneous and Distributed ComputingAdvanced TensorFlow ProgrammingRecommendation Systems using Factorization MachinesReinforcement LearningBook preview 