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Books by Timothy Masters

  • Deep Belief Nets in C++ and CUDA C: Volume 1

    Timothy Masters

    Product Code: BQDAY
    Paperback
    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.
    • £24.99
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  • Assessing and Improving Prediction and Classification

    Timothy Masters

    Product Code: BLWTY
    Paperback
    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. Many of 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 predictors.Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions.Improve numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing.Improve 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 coupling.Use information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promising.Use Monte-Carlo permutation methods to assess the role of good luck in performance results.Who 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.
    • £49.99
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  • Data Mining Algorithms in C++

    Timothy Masters

    Product Code: BLWSS
    Paperback
    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 learnMonte-Carlo permutation tests provide statistically sound assessment of relationships present in your data.Combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data.Feature weighting as regularized energy-based learning ranks variables according to their predictive power when there is too little data for traditional methods.The eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the data.Plotting regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is high, provides visual insight into anomalous relationships.Who this book is forThe techniques presented in this book and in the DATAMINE program will be useful to anyone 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.
    • £39.99
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