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Deep Belief Nets In C And Cuda C Volume 2


Deep Belief Nets In C And Cuda C Volume 2
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Deep Belief Nets In C And Cuda C Volume 2


Deep Belief Nets In C And Cuda C Volume 2
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Author : Timothy Masters
language : en
Publisher: Apress
Release Date : 2018-05-29

Deep Belief Nets In C And Cuda C Volume 2 written by Timothy Masters and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-29 with Computers categories.


Discover the essential building blocks of a common and powerful form of deep belief net: the autoencoder. You’ll take this topic beyond current usage by extending it to the complex domain for signal and image processing applications. Deep Belief Nets in C++ and CUDA C: Volume 2 also covers several algorithms for preprocessing time series and image data. These algorithms focus on the creation of complex-domain predictors that are suitable for input to a complex-domain autoencoder. Finally, you’ll learn a method for embedding class information in the input layer of a restricted Boltzmann machine. This facilitates generative display of samples from individual classes rather than the entire data distribution. The ability to see the features that the model has learned for each class separately can be invaluable. At each step this book provides you with intuitive motivation, a summary of the most important equations relevant to the topic, and highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. What You'll Learn Code for deep learning, neural networks, and AI using C++ and CUDA C Carry out signal preprocessing using simple transformations, Fourier transforms, Morlet wavelets, and more Use the Fourier Transform for image preprocessing Implement autoencoding via activation in the complex domain Work with algorithms for CUDA gradient computation Use the DEEP operating manual Who This Book Is For Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.



Deep Belief Nets In C And Cuda C Volume 3


Deep Belief Nets In C And Cuda C Volume 3
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Author : Timothy Masters
language : en
Publisher: Apress
Release Date : 2018-07-04

Deep Belief Nets In C And Cuda C Volume 3 written by Timothy Masters and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-04 with Computers categories.


Discover 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. At each step Deep Belief Nets in C++ and CUDA C: Volume 3 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. What You Will Learn Discover convolutional nets and how to use them Build deep feedforward nets using locally connected layers, pooling layers, and softmax outputs Master the various programming algorithms required Carry out multi-threaded gradient computations and memory allocations for this threading Work with CUDA code implementations of all core computations, including layer activations and gradient calculations Make use of the CONVNET program and manual to explore convolutional nets and case studies Who This Book Is For Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.



Deep Belief Nets In C And Cuda C


Deep Belief Nets In C And Cuda C
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Author : Timothy Masters
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-04-04

Deep Belief Nets In C And Cuda C written by Timothy Masters and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-04 with categories.


Deep belief nets are one of the most exciting recent developments in artificial intelligence. 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. A typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. This book presents the essential building blocks of a common and powerful form of deep belief net: convolutional nets. These models are especially useful for image processing applications. At each step the text provides 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 from the author's website. Source code for the complete CONVNET program is not available, as much of it is highly specialized Windows interface code. Readers are responsible for writing their own main program, with all interface routines. You may freely use all of the core convolutional net routines in this book, as long as you remember that it is experimental code that comes with absolutely no guaranty of correct operation.



Deep Belief Nets In C And Cuda C Volume 1


Deep Belief Nets In C And Cuda C Volume 1
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Author : Timothy Masters
language : en
Publisher: Apress
Release Date : 2018-04-23

Deep Belief Nets In C And Cuda C Volume 1 written by Timothy Masters and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-23 with Computers categories.


Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides 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. The first of three in a series on C++ and CUDA C deep learning and belief nets, Deep Belief Nets in C++ and CUDA C: Volume 1 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. As such, you’ll see that a typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. All the routines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines. What You Will Learn Employ deep learning using C++ and CUDA C Work with supervised feedforward networks Implement restricted Boltzmann machines Use generative samplings Discover why these are important Who This Book Is For Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.



Deep Belief Nets In C And Cuda C


Deep Belief Nets In C And Cuda C
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Author : Timothy Masters
language : en
Publisher: CreateSpace
Release Date : 2015-02-11

Deep Belief Nets In C And Cuda C written by Timothy Masters and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-11 with Computers categories.


Deep belief nets are one of the most exciting recent developments in artificial intelligence. 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. A typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. This book presents the essential building blocks of the most common forms of deep belief nets. At each step the text provides 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 DEEP program which implements these algorithms, are available for free download from the author's website.



Deep Belief Nets In C And Cuda C


Deep Belief Nets In C And Cuda C
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Author : Timothy Masters
language : en
Publisher:
Release Date : 2015

Deep Belief Nets In C And Cuda C written by Timothy Masters and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with C++ (Computer program language) categories.




Handbook Of Research On Big Data Storage And Visualization Techniques


Handbook Of Research On Big Data Storage And Visualization Techniques
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Author : Segall, Richard S.
language : en
Publisher: IGI Global
Release Date : 2018-01-05

Handbook Of Research On Big Data Storage And Visualization Techniques written by Segall, Richard S. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-05 with Computers categories.


The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data. The Handbook of Research on Big Data Storage and Visualization Techniques is a critical scholarly resource that explores big data analytics and technologies and their role in developing a broad understanding of issues pertaining to the use of big data in multidisciplinary fields. Featuring coverage on a broad range of topics, such as architecture patterns, programing systems, and computational energy, this publication is geared towards professionals, researchers, and students seeking current research and application topics on the subject.



C Cuda C


C Cuda C
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Author :
language : ko
Publisher:
Release Date : 2016

C Cuda C written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.




Pro Hadoop Data Analytics


Pro Hadoop Data Analytics
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Author : Kerry Koitzsch
language : en
Publisher: Apress
Release Date : 2016-12-29

Pro Hadoop Data Analytics written by Kerry Koitzsch and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-29 with Computers categories.


Learn advanced analytical techniques and leverage existing tool kits to make your analytic applications more powerful, precise, and efficient. This book provides the right combination of architecture, design, and implementation information to create analytical systems that go beyond the basics of classification, clustering, and recommendation. Pro Hadoop Data Analytics emphasizes best practices to ensure coherent, efficient development. A complete example system will be developed using standard third-party components that consist of the tool kits, libraries, visualization and reporting code, as well as support glue to provide a working and extensible end-to-end system. The book also highlights the importance of end-to-end, flexible, configurable, high-performance data pipeline systems with analytical components as well as appropriate visualization results. You'll discover the importance of mix-and-match or hybrid systems, using different analytical components in one application. This hybrid approach will be prominent in the examples. What You'll Learn Build big data analytic systems with the Hadoop ecosystem Use libraries, tool kits, and algorithms to make development easier and more effective Apply metrics to measure performance and efficiency of components and systems Connect to standard relational databases, noSQL data sources, and more Follow case studies with example components to create your own systems Who This Book Is For Software engineers, architects, and data scientists with an interest in the design and implementation of big data analytical systems using Hadoop, the Hadoop ecosystem, and other associated technologies.



Testing And Tuning Market Trading Systems


Testing And Tuning Market Trading Systems
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Author : Timothy Masters
language : en
Publisher: Apress
Release Date : 2018-10-26

Testing And Tuning Market Trading Systems written by Timothy Masters and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-26 with Computers categories.


Build, test, and tune financial, insurance or other market trading systems using C++ algorithms and statistics. You’ve had an idea and have done some preliminary experiments, and it looks promising. Where do you go from here? Well, this book discusses and dissects this case study approach. Seemingly good backtest performance isn't enough to justify trading real money. You need to perform rigorous statistical tests of the system's validity. Then, if basic tests confirm the quality of your idea, you need to tune your system, not just for best performance, but also for robust behavior in the face of inevitable market changes. Next, you need to quantify its expected future behavior, assessing how bad its real-life performance might actually be, and whether you can live with that. Finally, you need to find its theoretical performance limits so you know if its actual trades conform to this theoretical expectation, enabling you to dump the system if it does not live up to expectations. This book does not contain any sure-fire, guaranteed-riches trading systems. Those are a dime a dozen... But if you have a trading system, this book will provide you with a set of tools that will help you evaluate the potential value of your system, tweak it to improve its profitability, and monitor its on-going performance to detect deterioration before it fails catastrophically. Any serious market trader would do well to employ the methods described in this book. What You Will Learn See how the 'spaghetti-on-the-wall' approach to trading system development can be done legitimatelyDetect overfitting early in developmentEstimate the probability that your system's backtest results could have been due to just good luckRegularize a predictive model so it automatically selects an optimal subset of indicator candidatesRapidly find the global optimum for any type of parameterized trading systemAssess the ruggedness of your trading system against market changesEnhance the stationarity and information content of your proprietary indicatorsNest one layer of walkforward analysis inside another layer to account for selection bias in complex trading systemsCompute a lower bound on your system's mean future performanceBound expected periodic returns to detect on-going system deterioration before it becomes severeEstimate the probability of catastrophic drawdown Who This Book Is For Experienced C++ programmers, developers, and software engineers. Prior experience with rigorous statistical procedures to evaluate and maximize the quality of systems is recommended as well.