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  • [Place of publication not identified] : Apress  (3)
  • [New York, New York] : Apress  (1)
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  • 1
    Online Resource
    Online Resource
    Deep Belief Nets in C++ and CUDA C (2018)
    [Place of publication not identified] : Apress
    Details
    ISBN: 9781484236468
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Neural networks (Computer science) ; C++ (Computer program language) ; Machine learning ; Electronic books ; local ; Electronic books
    Abstract: 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.
    Note: Description based on online resource; title from cover (Safari, viewed June 22, 2018)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Deep belief nets in C++ and CUDA C (2018)
    [Place of publication not identified] : Apress
    Details
    ISBN: 9781484237212
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Neural networks (Computer science) ; C++ (Computer program language) ; Electronic books ; local ; Electronic books
    Abstract: 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.
    Note: Description based on online resource; title from cover (Safari, viewed August 7, 2018)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Testing and tuning market trading systems : algorithms in C++ (2018)
    [New York, New York] : Apress
    Details
    ISBN: 9781484241738
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: C++ (Computer program language) ; Computer algorithms ; Data mining ; Electronic books ; local ; Electronic books
    Abstract: 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 legitimately Detect overfitting early in development Estimate the probability that your system's backtest results could have been due to just good luck Regularize a predictive model so it automatically selects an optimal subset of indicator candidates Rapidly find the global optimum for any type of parameterized trading system Assess the ruggedness of your trading system against market changes Enhance the stationarity and information content of your proprietary indicators Nest one layer of walkforward analysis inside another layer to account for selection bias in complex trading systems Compute a lower bound on your system's mean future performance Bound expected periodic returns to detect on-going system deterioration before it becomes severe Estimate the probability of catastrophic drawdown Who This Book Is For Experienced C++ p...
    Note: Includes index. - Description based on online resource; title from title page (Safari, viewed December 3, 2018)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    Deep Belief Nets in C++ and CUDA C (2018)
    [Place of publication not identified] : Apress
    Details
    ISBN: 9781484235911
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Neural networks (Computer science) ; C++ (Computer program language) ; Electronic books ; local ; Electronic books
    Abstract: 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.
    Note: Description based on online resource; title from cover (Safari, viewed May 21, 2018)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
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