Best Upcoming Algorithms Books of March 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 March 2017

  • Windows Internals, Part 1: System architecture, processes, threads, memory management, and more (7th Edition)
    Windows Internals, Part 1: System architecture, processes, threads, memory management, and more (7th Edition)

    Delve inside Windows architecture and internals – and see how core components work behind the scenes. This classic guide has been fully updated for Windows10 and Windows Server 2012 R2, and now presents its coverage in two parts.

  • What Algorithms Want: Imagination in the Age of Computing (MIT Press) [Read online]
    What Algorithms Want: Imagination in the Age of Computing (MIT Press)

    We depend on — we believe in — algorithms to help us get a ride, choose which book to buy, execute a mathematical proof. Humans have always believed that certain invocations — the marriage vow, the shaman’s curse — do not merely describe the world but make it. In this book, Ed Finn considers how the algorithm — in practical terms, “a method for solving a problem” — has its roots not only in mathematical logic but also in cybernetics, philosophy, and magical thinking.

  • Real-World Algorithms: A Beginner’s Guide (MIT Press)
    Real-World Algorithms: A Beginner's Guide (MIT Press)

    The book presents algorithms simply and accessibly, without overwhelming readers or insulting their intelligence. Each chapter describes real problems and then presents algorithms to solve them. Real-World Algorithms can be used by students in disciplines from economics to applied sciences.

  • Algorithms For Dummies (For Dummies (Computer/Tech))
    Algorithms For Dummies (For Dummies (Computer/Tech))

    Discover how algorithms shape and impact our digital world All data, big or small, starts with algorithms. Algorithms are mathematical equations that determine what we see—based on our likes, dislikes, queries, views, interests, relationships, and more—online. Algorithms for Dummies is a clear and concise primer for everyday people who are interested in algorithms and how they impact our digital lives.

  • Combinatorics and Complexity of Partition Functions (Algorithms and Combinatorics)
    Combinatorics and Complexity of Partition Functions (Algorithms and Combinatorics)

    Partition functions arise in combinatorics and related problems of statistical physics as they encode in a succinct way the combinatorial structure of complicated systems. The main focus of the book is on efficient ways to compute (approximate) various partition functions, such as permanents, hafnians and their higher-dimensional versions, graph and hypergraph matching polynomials, the independence polynomial of a graph and partition functions enumerating 0-1 and integer points in polyhedra, which allows one to make algorithmic advances in otherwise intractable problems. The book unifies various, often quite recent, results scattered in the literature, concentrating on the three main approaches: scaling, interpolation and correlation decay.

  • 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.

  • Solving PDEs in Python: The FEniCS Tutorial I (Simula SpringerBriefs on Computing)
    Solving PDEs in Python: The FEniCS Tutorial I (Simula SpringerBriefs on Computing)

    This book offers a concise and gentle introduction to finite element programming in Python based on the popular FEniCS software library. Using a series of examples, including the Poisson equation, the equations of linear elasticity, the incompressible Navier–Stokes equations, and systems of nonlinear advection–diffusion–reaction equations, it guides readers through the essential steps to quickly solving a PDE in FEniCS, such as how to define a finite variational problem, how to set boundary conditions, how to solve linear and nonlinear systems, and how to visualize solutions and structure finite element Python programs. This book is open access under a CC BY license.

  • Numbers and Computers [Read online]
    Numbers and Computers

    This is a book about numbers and how those numbers are represented in and operated on by computers. Divided into two parts, the first deals with standard representations of integers and floating point numbers, while the second examines several other number representations. Topics covered include interval arithmetic, fixed-point numbers, big integers and rational arithmetic.

  • Think Like a Data Scientist: Tackle the data science process step-by-step
    Think Like a Data Scientist: Tackle the data science process step-by-step

    Data science is more than just a set of tools and techniques for extracting knowledge from data sets and data streams. Data science is also a process of getting from goals and questions to real, valuable outcomes by exploring, observing, and manipulating a world of data. Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems.

  • Reactive Machine Learning Systems
    Reactive Machine Learning Systems

    Machine learning applications autonomously reason about data at massive scale. But machine learning systems are different than other applications when it comes to testing, building, deploying, and monitoring. Reactive Machine Learning Systems teaches readers how to implement reactive design solutions in their machine learning systems to make them as reliable as a well-built web app.

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