Using parallel machines is difficult because of their inherent complexity and because their architecture changes frequently. This book presents an …
The Handbook of Computational Statistics - Concepts and Methods ist divided into 4 parts. It begins with an overview of …
Computationally intensive methods have become widely used both for statistical inference and for exploratory analyses of data. The methods of …
Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization …
The real challenge of programming isnt learning a languages syntax—its learning to creatively solve problems so you can build something …
The real challenge of programming isn't learning a language's syntax - it's learning to creatively solve problems so you can …
Instead of learning by trial and error, you can learn problem solving in a systematic way. That’s what this book …
The definitive book on the foundations and theory of database systems, including advanced topics not presented in any other survey …
Review the fundamental constructs in C# using Q&As and program segments to boost your confidence and gain expertise. This book …
A high-level overview of networking, data science and computer security. Designed for readers who don't care for academic formalities, it's …
Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and …
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This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common …
Key Features• Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural …
