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

Deep Belief Nets in C   and CUDA C  Volume 2 PDF
Author: Timothy Masters
Publisher: Apress
ISBN: 1484236467
Size: 77.49 MB
Format: PDF
Category : Computers
Languages : en
Pages : 258
View: 7677

Get Book

Deep Belief Nets In C And Cuda C Volume 2 Book Description:

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 PDF Download

Deep Belief Nets in C   and CUDA C  Volume 3 PDF
Author: Timothy Masters
Publisher: Apress
ISBN: 1484237218
Size: 40.95 MB
Format: PDF
Category : Computers
Languages : en
Pages : 176
View: 2563

Get Book

Deep Belief Nets In C And Cuda C Volume 3 Book Description:

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 PDF Download

Deep Belief Nets in C   and Cuda C PDF
Author: Timothy Masters
Publisher: CreateSpace
ISBN: 9781514365991
Size: 69.40 MB
Format: PDF, Docs
Category :
Languages : en
Pages : 242
View: 3218

Get Book

Deep Belief Nets In C And Cuda C Book Description:

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: the autoencoder. Volume II takes this topic beyond current usage by extending it to the complex domain, which is useful for many signal and image processing applications. Several algorithms for preprocessing time series and image data are also presented. These algorithms focus on the creation of complex-domain predictors that are suitable for input to a complex-domain autoencoder. Finally, this book provides 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 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 Volume 1 PDF Download

Deep Belief Nets in C   and CUDA C  Volume 1 PDF
Author: Timothy Masters
Publisher: Apress
ISBN: 1484235916
Size: 23.91 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 219
View: 2526

Get Book

Deep Belief Nets In C And Cuda C Volume 1 Book Description:

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.

Machine Learning For Adaptive Many Core Machines A Practical Approach PDF Download

Machine Learning for Adaptive Many Core Machines   A Practical Approach PDF
Author: Noel Lopes
Publisher: Springer
ISBN: 3319069381
Size: 11.61 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 241
View: 2536

Get Book

Machine Learning For Adaptive Many Core Machines A Practical Approach Book Description:

The overwhelming data produced everyday and the increasing performance and cost requirements of applications are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data. This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind. It presents a series of new techniques to enhance, scale and distribute data in a Big Learning framework. It is not intended to be a comprehensive survey of the state of the art of the whole field of machine learning for Big Data. Its purpose is less ambitious and more practical: to explain and illustrate existing and novel GPU-based ML algorithms, not viewed as a universal solution for the Big Data challenges but rather as part of the answer, which may require the use of different strategies coupled together.

Testing And Tuning Market Trading Systems PDF Download

Testing and Tuning Market Trading Systems PDF
Author: Timothy Masters
Publisher: Apress
ISBN: 1484241738
Size: 67.55 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 321
View: 3634

Get Book

Testing And Tuning Market Trading Systems Book Description:

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++ programmers, developers, and software engineers. Prior experience with rigorous statistical procedures to evaluate and maximize the quality of systems is recommended as well.

Modern Stereogram Algorithms For Art And Scientific Visualization PDF Download

Modern Stereogram Algorithms for Art and Scientific Visualization PDF
Author: Timothy Masters
Publisher: Createspace Independent Publishing Platform
ISBN: 9781719097406
Size: 20.56 MB
Format: PDF, Kindle
Category :
Languages : en
Pages : 154
View: 4657

Get Book

Modern Stereogram Algorithms For Art And Scientific Visualization Book Description:

Imagine looking at a picture on a printed page or computer screen, adjusting your eyes in a manner that most people can learn easily, and suddenly having objects pop out at you in vivid 3D. Many people have already experienced this with the Magic Eye and related posters that were massively popular in the 90's. But what is not so well known is that this single-image stereogram technology has come a long way since those early days. The big breakthrough came when algorithms were discovered that could map textures onto the surface of single-image stereograms. I believe this is the only available book that delves deeply into stereogram algorithms, including highly documented C++ source code. These algorithms can be used to great effect by artists to create works of art that are far beyond the crude stereograms of yesteryear. Perhaps even more importantly, the ability to display depth maps in clear stereo using only a single printed image can be invaluable for scientific presentations. This book is an essential resource for anyone writing programs for stereogram generation.

Artificial Neural Networks Icann 2010 PDF Download

Artificial Neural Networks   ICANN 2010 PDF
Author: Konstantinos Diamantaras
Publisher: Springer
ISBN: 3642158250
Size: 75.51 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 575
View: 5730

Get Book

Artificial Neural Networks Icann 2010 Book Description:

th This volume is part of the three-volume proceedings of the 20 International Conference on Arti?cial Neural Networks (ICANN 2010) that was held in Th- saloniki, Greece during September 15–18, 2010. ICANN is an annual meeting sponsored by the European Neural Network Society (ENNS) in cooperation with the International Neural Network So- ety (INNS) and the Japanese Neural Network Society (JNNS). This series of conferences has been held annually since 1991 in Europe, covering the ?eld of neurocomputing, learning systems and other related areas. As in the past 19 events, ICANN 2010 provided a distinguished, lively and interdisciplinary discussion forum for researches and scientists from around the globe. Ito?eredagoodchanceto discussthe latestadvancesofresearchandalso all the developments and applications in the area of Arti?cial Neural Networks (ANNs). ANNs provide an information processing structure inspired by biolo- cal nervous systems and they consist of a large number of highly interconnected processing elements (neurons). Each neuron is a simple processor with a limited computing capacity typically restricted to a rule for combining input signals (utilizing an activation function) in order to calculate the output one. Output signalsmaybesenttootherunitsalongconnectionsknownasweightsthatexcite or inhibit the signal being communicated. ANNs have the ability “to learn” by example (a large volume of cases) through several iterations without requiring a priori ?xed knowledge of the relationships between process parameters.