Timothy Masters – författare

Discover hidden relationships among the variables in your data, and learn how to exploit these relationships.  This book presents a collection of data-mining algorithms that are effective in a wide variety of prediction and classification applications.  All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code.Many of these techniques are recent developments, still not in widespread use.  Others are standard algorithms given a fresh look.  In every case, the focus is on practical applicability, with all code written in such a way that it can easily be included into any program.  The Windows-based DATAMINE program lets you experiment with the techniques before incorporating them into your own work.What You'll LearnUse Monte-Carlo permutation tests to provide statistically sound assessments of relationships present in your dataDiscover how combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the dataWork with feature weighting as regularized energy-based learning to rank variables according to their predictive power when there is too little data for traditional methodsSee how the eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the dataPlot regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is highWho This Book Is ForAnyone interested in discovering and exploiting relationships among variables.  Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.

Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting.  This book presents many important techniques for building powerful, robust models and quantifying their expected behavior when put to work in your application.Considerable attention is given to information theory, especially as it relates to discovering and exploiting relationships between variables employed by your models.  This presentation of an often confusing subject avoids advanced mathematics, focusing instead on concepts easily understood by those with modest background in mathematics.All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code.  Manyof these techniques are recent developments, still not in widespread use.  Others are standard algorithms given a fresh look.  In every case, the emphasis is on practical applicability, with all code written in such a way that it can easily be included in any program.What You'll LearnCompute entropy to detect problematic predictorsImprove numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothingCarry out classification decisions using Borda counts, MinMax and MaxMin rules, union and intersection rules, logistic regression, selection by local accuracy, maximization of the fuzzy integral, and pairwise couplingHarness information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promisingUse Monte-Carlo permutation methods to assessthe role of good luck in performance resultsCompute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisionsWho This Book is ForAnyone who creates prediction or classification models will find a wealth of useful algorithms in this book.  Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.

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 theroutines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines. What You Will LearnEmploy deep learning using C++ and CUDA CWork with supervised feedforward networks Implement restricted Boltzmann machines Use generative samplingsDiscover why these are importantWho This Book Is ForThose who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.

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 LearnCode for deep learning, neural networks, and AI using C++ and CUDA CCarry out signal preprocessing using simple transformations, Fourier transforms, Morlet wavelets, and moreUse the Fourier Transform for image preprocessingImplement autoencoding via activation in the complex domainWork with algorithms for CUDA gradient computationUse the DEEP operating manualWho This Book Is ForThose who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.

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 LearnDiscover convolutional nets and how to use themBuild deep feedforward nets using locally connected layers, pooling layers, and softmax outputsMaster the various programming algorithms requiredCarry out multi-threaded gradient computations and memory allocations for this threadingWork with CUDA code implementations of all core computations, including layer activations and gradient calculationsMake use of the CONVNET program and manual to explore convolutional nets and case studiesWho This Book Is ForThose who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.

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 liveup 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 LearnSee 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 drawdownWho This Book Is ForExperienced C++ programmers, developers, and software engineers.  Prior experience with rigorous statistical procedures to evaluate and maximize the quality of systems is recommended as well.