{"product_id":"deep-belief-nets-in-c-and-cuda-c-volume-3-convolutional-nets-paperback","title":"Deep Belief Nets in C++ and Cuda C: Volume 3: Convolutional Nets - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eTimothy Masters\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003eDiscover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a 'thought process' that is capable of learning abstract concepts built from simpler primitives. These models are especially useful for image processing applications. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eAt each step \u003ci\u003eDeep Belief Nets in C++ and CUDA C: Volume 3\u003c\/i\u003e presents intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. Source code for all routines presented in the book, and the executable CONVNET program which implements these algorithms, are available for free download. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eWhat You Will Learn\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eDiscover convolutional nets and how to use them\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eBuild deep feedforward nets using locally connected layers, pooling layers, and softmax outputs\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eMaster the various programming algorithms required\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eCarry out multi-threaded gradient computations and memory allocations for this threading\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eWork with CUDA code implementations of all core computations, including layer activations and gradient calculations\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eMake use of the CONVNET program and manual to explore convolutional nets and case studies\u003cbr\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003e\u003cbr\u003e\u003c\/b\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003cbr\u003eThose who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended. \u003cp\u003e\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003eDiscover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a 'thought process' that is capable of learning abstract concepts built from simpler primitives. These models are especially useful for image processing applications. \u003cbr\u003eAt each step \u003ci\u003eDeep Belief Nets in C++ and CUDA C: Volume 3\u003c\/i\u003e presents intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. Source code for all routines presented in the book, and the executable CONVNET program which implements these algorithms, are available for free download. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eYou will: \u003cul\u003e\n\u003cli\u003eDiscover convolutional nets and how to use them\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eBuild deep feedforward nets using locally connected layers, pooling layers, and softmax outputs\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eMaster the various programming algorithms required\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eCarry out multi-threaded gradient computations and memory allocations for this threading\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eWork with CUDA code implementations of all core computations, including layer activations and gradient calculations\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eMake use of the CONVNET program and manual to explore convolutional nets and case studies\u003cbr\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eTimothy Masters\u003c\/b\u003e received a PhD in mathematical statistics with a specialization in numerical computing. Since then he has continuously worked as an independent consultant for government and industry. His early research involved automated feature detection in high-altitude photographs while he developed applications for flood and drought prediction, detection of hidden missile silos, and identification of threatening military vehicles. Later he worked with medical researchers in the development of computer algorithms for distinguishing between benign and malignant cells in needle biopsies. For the last twenty years he has focused primarily on methods for evaluating automated financial market trading systems. He has authored five books on practical applications of predictive modeling: \u003ci\u003ePractical Neural Network Recipes in C++\u003c\/i\u003e (Academic Press, 1993); \u003ci\u003eSignal and Image Processing with Neural Networks\u003c\/i\u003e (Wiley, 1994); \u003ci\u003eAdvanced Algorithms for Neural Networks\u003c\/i\u003e (Wiley, 1995); \u003ci\u003eNeural, Novel, and Hybrid Algorithms for Time Series Prediction\u003c\/i\u003e (Wiley, 1995); \u003ci\u003eData Mining Algorithms in C++\u003c\/i\u003e (Apress, 2018); Assessing and Improving Prediction and Classification (Apress, 2018); \u003ci\u003eDeep Belief Nets in C++ and CUDA C: Volume 1\u003c\/i\u003e (Apress, 2018); and \u003ci\u003eDeep Belief Nets in C++ and CUDA C: Volume 2\u003c\/i\u003e (Apress, 2018).\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 176\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.4 x 10 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e July 05, 2018\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":52492495946035,"sku":"9781484237205","price":77.18,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0300\/5595\/6612\/files\/aXh3NjZlWkpLYkhJVGtBOGszMS9Sdz09.webp?v=1759935526","url":"https:\/\/www.vysn.com\/en-ca\/products\/deep-belief-nets-in-c-and-cuda-c-volume-3-convolutional-nets-paperback","provider":"VYSN","version":"1.0","type":"link"}