Best Upcoming Algorithms Books of July 2017

Every month, new books are released, and we’d like to share the ones with you we think should not be missed. This time, we’re focussing on Algorithms. Please let us know what you think, and happy reading!

Best Upcoming Algorithms Books of July 2017

  • Advanced Analytics with Spark: Patterns for Learning from Data at Scale
    Advanced Analytics with Spark: Patterns for Learning from Data at Scale

    In the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. Updated for Spark 2. With this book, you will: Familiarize yourself with the Spark programming model Become comfortable within the Spark ecosystem Learn general approaches in data science Examine complete implementations that analyze large public data sets Discover which machine learning tools make sense for particular problems Acquire code that can be adapted to many uses.

  • Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis
    Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis

    Newly added chapters and sections cover topics including normal distributions, sample complexity, VC dimension, Rademacher complexity, power laws and related distributions, cuckoo hashing, and the Lovasz Local Lemma. Material relevant to machine learning and big data analysis enables students to learn modern techniques and applications. Among the many new exercises and examples are programming-related exercises that provide students with excellent training in solving relevant problems.

  • Once Upon an Algorithm: How Stories Explain Computing (MIT Press)
    Once Upon an Algorithm: How Stories Explain Computing (MIT Press)

    In Once Upon an Algorithm, Martin Erwig explains computation as something that takes place beyond electronic computers, and computer science as the study of systematic problem solving. Erwig points out that many daily activities involve problem solving. This simple daily routine solves a recurring problem through a series of well-defined steps.

  • Machine Learning with TensorFlow
    Machine Learning with TensorFlow

    The answer is TensorFlow, a new open source machine learning library from Google. Machine Learning with TensorFlow teaches readers about machine learning algorithms and how to implement solutions with TensorFlow. Each chapter zooms into a prominent example of machine learning.

  • Euclidean Distance Geometry: An Introduction (Springer Undergraduate Texts in Mathematics and Technology)
    Euclidean Distance Geometry: An Introduction (Springer Undergraduate Texts in Mathematics and Technology)

    This textbook, the first of its kind, presents the fundamentals of distance geometry: theory, useful methodologies for obtaining solutions, and real world applications. Descriptive graphics, examples, and problems, accompany the real gems of the text, namely the applications in visualization of graphs, localization of sensor networks, protein conformation from distance data, clock synchronization protocols, robotics, and control of unmanned underwater vehicles, to name several. Aimed at intermediate undergraduates, beginning graduate students, researchers, and practitioners, the reader with a basic knowledge of linear algebra will gain an understanding of the basic theories of distance geometry and why they work in real life.

  • Non-Convex Multi-Objective Optimization (Springer Optimization and Its Applications)
    Non-Convex Multi-Objective Optimization (Springer Optimization and Its Applications)

    Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach.

  • Bored and Brilliant: How Spacing Out Can Unlock Your Most Productive and Creative Self
    Bored and Brilliant: How Spacing Out Can Unlock Your Most Productive and Creative Self

    Zomorodi also explores why putting greater emphasis on “doing nothing” is vital in an age of constant notifications and digital distractions. Bored and Brilliant is about living smarter and better within a digital world. Bored and Brilliant teaches us how to align our gadget use with what we hold dear and true and find equilibrium in this new digital ecosystem.

  • Handbook of Discrete and Combinatorial Mathematics, Second Edition (Discrete Mathematics and Its Applications)
    Handbook of Discrete and Combinatorial Mathematics, Second Edition (Discrete Mathematics and Its Applications)

    Providing a ready reference for practitioners in the field, the Handbook of Discrete and Combinatorial Mathematics, Second Edition presents additional material on Google’s matrix, random graphs, geometric graphs, computational topology, and other key topics. New chapters highlight essential background information on bioinformatics and computational geometry. Each chapter includes a glossary, definitions, facts, examples, algorithms, major applications, and references.

  • Coding for Parents: Everything You Need to Know to Confidently Help with Homework
    Coding for Parents: Everything You Need to Know to Confidently Help with Homework

    Coding for Parents teaches you what you need to know. . Unlock the mysteries of coding with this easy-to-follow and well-illustrated guide—and help your kids ace their coding homework.

  • Methods in Algorithmic Analysis (Chapman & Hall/CRC Computer and Information Science Series)
    Methods in Algorithmic Analysis (Chapman & Hall/CRC Computer and Information Science Series)

    Explores the Impact of the Analysis of Algorithms on Many Areas within and beyond Computer Science A flexible, interactive teaching format enhanced by a large selection of examples and exercises Developed from the author’s own graduate-level course, Methods in Algorithmic Analysis presents numerous theories, techniques, and methods used for analyzing algorithms. It exposes students to mathematical techniques and methods that are practical and relevant to theoretical aspects of computer science. It explores the role of recurrences in computer science, numerical analysis, engineering, and discrete mathematics applications.

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