K. Dewdney's The Turing Omnibus.Updated and expanded, The Turing Omnibus offers 66 concise, brilliantly written articles on the major points of interest in computer science theory, technology, and applications. New for this tour: updated information on algorithms, detecting primes, noncomputable functions, and self-replicating computers--plus completely new sections on the Mandelbrot set, genetic algorithms, the Newton-Raphson Method, neural networks that learn, DOS systems for personal computers, and computer viruses.Contents:1 Algorithms 2 Finite Automata 3 Systems of Logic 4 Simulation 5 Godel's Theorem 6 Game Trees 7 The Chomsky Hierarchy 8 Random Numbers 9 Mathematical Research 10 Program Correctness 11 Search Trees 12 Error-Corecting Codes 13 Boolean Logic 14 Regular Languages 15 Time and Space Complexity 16 Genetic Algorithms 17 The Random Access Machine 18 Spline Curves 19 Computer Vision 20 Karnaugh Maps 21 The Newton-Raphson Method 22 Minimum Spanning Trees 23 Generative Grammars 24 Recursion 25 Fast Multiplication 26 Nondeterminism 27 Perceptrons 28 Encoders and Multiplexers 29 CAT Scanning 30 The Partition Problem 31 Turing Machines 32 The Fast Fourier Transform 33 Analog Computing 34 Satisfiability 35 Sequential Sorting 36 Neural Networks That Learn 37 Public Key Cryptography 38 Sequential Cirucits 39 Noncomputerable Functions 40 Heaps and Merges 41 NP-Completeness 42 Number Systems for Computing 43 Storage by Hashing 44 Cellular Automata 45 Cook's Theorem 46 Self-Replicating Computers 47 Storing Images 48 The SCRAM 49 Shannon's Theory 50 Detecting Primes 51 Universal Turing Machines 52 Text Compression 53 Disk Operating Systems 54 NP-Complete Problems 55 Iteration and Recursion 56 VLSI Computers 57 Linear Programming 58 Predicate Calculus 59 The Halting Problem 60 Computer Viruses 61 Searching Strings 62 Parallel Computing 63 The Word Problem 64 Logic Programming 65 Relational Data Bases 66 Church's Thesis

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

    Smoking has gone from doctor recommended to deadly. We used to think the Earth was the center of the universe and that Pluto was a planet. For decades, we were convinced that the brontosaurus was a real dinosaur. In short, what we know about the world is constantly changing.   But it turns out there’s an order to the state of knowledge, an explanation for how we know what we know. Samuel Arbesman is an expert in the field of scientometrics—literally the science of science. Knowl­edge in most fields evolves systematically and predict­ably, and this evolution unfolds in a fascinating way that can have a powerful impact on our lives.   Doctors with a rough idea of when their knowl­edge is likely to expire can be better equipped to keep up with the latest research. Companies and govern­ments that understand how long new discoveries take to develop can improve decisions about allocating resources. And by tracing how and when language changes, each of us can better bridge gen­erational gaps in slang and dialect.   Just as we know that a chunk of uranium can break down in a measurable amount of time—a radioactive half-life—so too any given field’s change in knowledge can be measured concretely. We can know when facts in aggregate are obsolete, the rate at which new facts are created, and even how facts spread.   Arbesman takes us through a wide variety of fields, including those that change quickly, over the course of a few years, or over the span of centuries. He shows that much of what we know consists of “mesofacts”—facts that change at a middle timescale, often over a single human lifetime. Throughout, he of­fers intriguing examples about the face of knowledge: what English majors can learn from a statistical analysis of The Canterbury Tales, why it’s so hard to measure a mountain, and why so many parents still tell kids to eat their spinach because it’s rich in iron.   The Half-life of Facts is a riveting journey into the counterintuitive fabric of knowledge. It can help us find new ways to measure the world while accepting the limits of how much we can know with certainty.

    It guides you step by step from simple tests to tests that are maintainable, readable, and trustworthy. It covers advanced subjects like mocks, stubs, and frameworks such as Typemock Isolator and Rhino Mocks. And you'll learn about advanced test patterns and organization, working with legacy code and even untestable code. The book discusses tools you need when testing databases and other technologies. It's written for .NET developers but others will also benefit from this book.Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.Table of ContentsThe basics of unit testingA first unit testUsing stubs to break dependenciesInteraction testing using mock objectsIsolation (mock object) frameworksTest hierarchies and organizationThe pillars of good testsIntegrating unit testing into the organizationWorking with legacy code

    From Mary, Queen of Scots, trapped by her own code, to the Navajo Code Talkers who helped the Allies win World War II, to the incredible (and incredibly simple) logisitical breakthrough that made Internet commerce secure, The Code Book tells the story of the most powerful intellectual weapon ever known: secrecy.Throughout the text are clear technical and mathematical explanations, and portraits of the remarkable personalities who wrote and broke the world’s most difficult codes. Accessible, compelling, and remarkably far-reaching, this book will forever alter your view of history and what drives it. It will also make you wonder how private that e-mail you just sent really is.