Geo.Engineerings

Alan Graham ... 320 pages - Publisher: Teach Yourself; (April, 2017) ... Language: English - AmazonSIN: B01LZ6WZXS.

Do you need to gain confidence with handling numbers and formulae? Do you want a clear, step-by-step guide to the key concepts and principles of statistics? Nearly all aspects of our lives can be subject to statistical analysis. Statistics: An Introduction shows you how to interpret, analyze and present figures. Assuming minimal knowledge of maths and using examples from a wide variety of everyday contexts, this book makes often complex concepts and techniques easy to get to grips with. This new edition has been fully updated. Whether you want to understand the statistics that you are bombarded with every day or are a student or professional coming to statistics from a wide range of disciplines, Statistics: An Introduction covers it all.

Nilanjan Dey ... 266 pages - Publisher: Springer; (November, 2019) ... Language: English - AmazonSIN: B0818MWNQJ.

The book discusses advantages of the firefly algorithm over other well-known metaheuristic algorithms in various engineering studies. The book provides a brief outline of various application-oriented problem solving methods, like economic emission load dispatch problem, designing a fully digital controlled reconfigurable switched beam nonconcentric ring array antenna, image segmentation, span minimization in permutation flow shop scheduling, multi-objective load dispatch problems, image compression, etc., using FA and its variants. It also covers the use of the firefly algorithm to select features, as research has shown that the firefly algorithm generates precise and optimal results in terms of time and optimality. In addition, the book also explores the potential of the firefly algorithm to provide a solution to traveling salesman problem, graph coloring problem, etc.

Seth Weidman ... 253 pages - Publisher: O'Reilly Media; (September, 2019) ... Language: English - AmazonSIN: B07XL53Y4C.

With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You’ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way. Author Seth Weidman shows you how neural networks work using a first principles approach. You’ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you’ll be set up for success on all future deep learning projects.

This book provides: Extremely clear and thorough mental models—accompanied by working code examples and mathematical explanations—for understanding neural networks + Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework + Working implementations and clear-cut explanations of convolutional and recurrent neural networks + Implementation of these neural network concepts using the popular PyTorch framework.