Technology: Recent submissions
Now showing items 1-20 of 3030
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Principles of Data Mining (3 ed.)
(Springer, 2016)This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other ... -
Data Structures and Algorithms with Python
(Springer, 2015)This clearly structured and easy to read textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents ... -
Fundamentals of Robotic Mechanical Systems: Theory, Methods, and Algorithms (4 ed.)
(Springer, 2014)The 4th edition includes updated and additional examples and exercises on the core fundamental concepts of mechanics, robots, and kinematics of serial robots. New images of CAD models and physical robots help to motivate ... -
Introduction to Partial Differential Equations
(Springer, 2016)This modern take on partial differential equations does not require knowledge beyond vector calculus and linear algebra. The author focuses on the most important classical partial differential equations, including conservation ... -
Linear and Nonlinear Programming (4 ed.)
(Springer, 2016)This new edition covers the central concepts of practical optimization techniques, with an emphasis on methods that are both state-of-the-art and popular. Again a connection between the purely analytical character of an ... -
Excel Data Analysis: Modeling and Simulation (2 ed.)
(Springer, 2019)This book offers a comprehensive and readable introduction to modern business and data analytics. It is based on the use of Excel, a tool that virtually all students and professionals have access to. The explanations are ... -
Introduction to Evolutionary Computing (2 ed.)
(Springer, 2015)The overall structure of this new edition is three-tier: Part I presents the basics, Part II is concerned with methodological issues, and Part III discusses advanced topics. In the second edition the authors have reorganized ... -
Neural Networks and Deep Learning: A Textbook
(Springer, 2018)This book covers both classical and modern models in deep learning. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special ... -
Introduction to Artificial Intelligence (2 ed.)
(Springer, 2017)This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical ... -
Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence
(Springer, 2018)This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular ... -
A Beginner’s Guide to R
(Springer, 2009)Based on their extensive experience with teaching R and statistics to applied scientists, the authors provide a beginner's guide to R. To avoid the difficulty of teaching R and statistics at the same time, statistical ... -
Bayesian Essentials with R (2 ed.)
(Springer, 2014)This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called ... -
Foundations of Programming Languages (2 ed.)
(Springer, 2017)This clearly written textbook provides an accessible introduction to the three programming paradigms of object-oriented/imperative, functional, and logic programming. Highly interactive in style, the text encourages learning ... -
Concise Guide to Databases: A Practical Introduction
(Springer, 2013)Modern businesses depend on data for their very survival, creating a need for sophisticated databases and database technologies to help store, organise and transport their valuable data. This easy-to-read textbook/reference ... -
Digital Image Processing: An Algorithmic Introduction Using Java (2 ed.)
(Springer, 2016)This modern, self-contained textbook provides an accessible introduction to the field from the perspective of a practicing programmer, supporting a detailed presentation of the fundamental concepts and techniques with ... -
Applied Predictive Modeling
(Springer, 2013)This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an ... -
Python For ArcGIS
(Springer, 2015)This book introduces Python scripting for geographic information science (GIS) workflow optimization using ArcGIS. It builds essential programming skills for automating GIS analysis. Over 200 sample Python scripts and 175 ... -
Object-Oriented Analysis, Design and Implementation: An Integrated Approach (2 ed.)
(Springer, 2015)The second edition of this textbook includes revisions based on the feedback on the first edition. In a new chapter the authors provide a concise introduction to the remainder of UML diagrams, adopting the same holistic ... -
Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications
(Springer, 2017)This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and ... -
Machine Learning in Medicine - A Complete Overview
(Springer, 2015)The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector. It was written as a training companion, and as a must-read, not only for physicians and ...
