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Many examples are given, with a liberal use of color graphics. Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. It contains many exercises, all with worked solutions, including complete computer code. This book is a hands-on introduction to the Python programming language, written for people who have no experience with programming whatsoever.

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You don't need to worry. Learning how to program a computer is far from impossible. This book is a practical guide to getting started with graph algorithms for developers and data scientists who have experience using Apache Spark or Neo4j.

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If you want to learn how to build efficient user interfaces with React, this is your book. This book explores what Flutter has to offer, where it came from, and where it's going. You will begin by getting a solid foundation of Flutter knowledge, and building on it immediately by constructing two more traditional productivity apps.

The goal of this book is to document our process, share our research and try to organize our experiences into a single living document. Too be honest, this is a pretty lofty goal. We may not achieve this goal, but hopefully we can get some helpful thoughts down.


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With an easy to understand format using graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics. Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. This book is a hands-on manual for learning how to design systems using the STM32 F1 family of microcontrollers, focuses on developing code to utilize the various peripherals available in STM32 F1 micro-controllers and in particular the STM32VL Discovery board.


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In this book, the author proposes cryptographic building blocks for the design of digital certificates that preserve privacy without sacrificing security. This book is focusing on the experience of particular disciplines in applying computational methods to humanities research problems; the basic principles of humanities computing across applications and disciplines; specific applications and methods; and production, dissemination, and archiving.

This book provides you with proven advice, tips and strategies to help you through the job process including: resume writing, strategies, the point of the cover letter, creating that professional image through your social networks, etc. Book Site. Want to know Runways information of a particular airport? Click here to find out. Graph Databases for Beginners Bryce Merkl Sasaki, et al Post under Non-relational and NoSQL Databases on Sat Sep 14, This book takes those new to the world of connected data through the basics of graph technology - whether you're a business executive or a seasoned developer, something has led you on the quest to learn more about graph technology - what it does for you.

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Post under Machine Learning and Statistics on Sat Sep 14, This book describes the important ideas in these areas in a common conceptual framework. Bayesian Methods for Statistical Analysis Borek Puza Post under Statistics and Mathematical Statistics on Sat Sep 14, Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. An Introduction to Neural Networks Kevin Gurney Post under Neural Networks and Deep Learning on Sat Sep 07, With an easy to understand format using graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics.

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Foundation Mathematics for Computer Science

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