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R Deep Learning Essentials
Key Features
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Harness the ability to build algorithms for unsupervised data using deep learning concepts with R
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Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models
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Build models relating to neural
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Performance Tuning for Linux(R) ServersProven techniques for optimizing web, file, print, database, and application servers Practical introductions to open source performance management tools Easy-to-understand examples based on Red Hat Enterprise Linux and Novell SUSE Linux Enterprise ServerAn indispensable guide to maximizing Linux system and application performanceFrom Wall... | | Essentials of Radio Wave Propagation (The Cambridge Wireless Essentials Series)If you need to maximize efficiency in wireless network planning, this quick reference guide to an increasingly complex and difficult topic is for you. Using real-world case studies, practical problems and minimum mathematics, the author explains simply and clearly how to predict signal strengths in a variety of situations. More sophisticated... | | |
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