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Puzzles for Programmers and Pros
Aimed at both working programmers who are applying for a job where puzzles are an integral part of the interview, as well as techies who just love a good puzzle, this book offers a cache of exciting puzzles Features a new series of puzzles, never before published, called elimination puzzles that have a pedagogical aim of helping the reader solve an entire class of Sudoku-like puzzles Provides the tools to solve the puzzles by hand and computer The first part of each chapter presents a puzzle; the second part shows readers how to solve several classes of puzzles algorithmically; the third part asks the reader to solve a mystery involving codes, puzzles, and geography Comes with a unique bonus: if readers actually solve the mystery, they have a chance to win a prize, which will be promoted on wrox.com!
Puzzles for Programmers and Pros
Aimed at both working programmers who are applying for a job where puzzles are an integral part of the interview, as well as techies who just love a good puzzle, this book offers a cache of exciting puzzles.; Features a new series of puzzles.
Puzzling Adventures: Tales of Strategy, Logic, and Mathematical Skill
Hours of recreational reckoning. Collected and enhanced from Dennis Shasha's popular Scientific American column, here are thirty-six of the most innovative and emotive mathematical puzzles ever to appear in its pages. Edgy, challenging and representing the ultimate in recreational mathematical games, Puzzling Adventures dares the reader to work out the logic underlying venture fund investments, escape a Minotaur, catch a polar bear, play power politics, work out if a witness is lying, spy on contraband traders and verify DNA. An encrypted set of stories and commentary float above the puzzles. They need decrypting to discover their hints. The hints lead to a surprise—if the reader can work them out.
The Puzzler's Elusion
Genius mathematician, Dr. Dennis Shasha, here sets out his latest book-length mind-twister. Made up of many smaller segments, some of which can be solved by ten year-olds and some which are more challenging, the detective work requires no more than high school geometry and junior high school algebra. In every case, imagination trumps knowledge. The puzzles are set in a larger story of a mathematical detective named Dr. Ecco, his nephew and niece, and Professor Scarlet, the narrator. Scarlet is essentially the Watson to Dr. Ecco's Sherlock Holmes, asking the questions a reader might ask. Each puzzle is posed in a plausible if imaginary real-life setting. There are no hidden facts, no abduction here, just deductive logic and mathematical thought. Overlaying these puzzles are the ramblings of Dr Ecco's old nemesis, Benjamin Baskerhound. He seems to be on the run, but he's trying to tell Ecco his whereabouts in a way that only Ecco will understand. The evidence builds up and readers are invited to send in their solutions. The winner will receive a pre-paid trip to the home of modern mathematics, Sir Isaac Newton's Greenwich Observatory.
Statistics is Easy
Computational analysis of natural science experiments often confronts noisy data due to natural variability in environment or measurement. Drawing conclusions in the face of such noise entails a statistical analysis. Parametric statistical methods assume that the data is a sample from a population that can be characterized by a specific distribution (e.g., a normal distribution). When the assumption is true, parametric approaches can lead to high confidence predictions. However, in many cases particular distribution assumptions do not hold. In that case, assuming a distribution may yield false conclusions. The companion book Statistics is Easy, gave a (nearly) equation-free introduction to nonparametric (i.e., no distribution assumption) statistical methods. The present book applies data preparation, machine learning, and nonparametric statistics to three quite different life science datasets. We provide the code as applied to each dataset in both R and Python 3. We also include exercises for self-study or classroom use.
Out of Their Minds
Out of their minds and the force of their imagination, men have created countless beings, from demons and monsters of legend to comic-strip characters. What if their world were real - if dragons, devils and Don Quixote hobnobbed with Dagwood Bumstead and Charlie Brown? Such a world would have its fascinations . . . and its dreadful perils - if it existed. Horton Smith found out that it did - and that he was right in the middle of it!
Automated Verification of Concurrent Search Structures
Search structures support the fundamental data storage primitives on key-value pairs: insert a pair, delete by key, search by key, and update the value associated with a key. Concurrent search structures are parallel algorithms to speed access to search structures on multicore and distributed servers. These sophisticated algorithms perform fine-grained synchronization between threads, making them notoriously difficult to design correctly. Indeed, bugs have been found both in actual implementations and in the designs proposed by experts in peer-reviewed publications. The rapid development and deployment of these concurrent algorithms has resulted in a rift between the algorithms that can be verified by the state-of-the-art techniques and those being developed and used today. The goal of this book is to show how to bridge this gap in order to bring the certified safety of formal verification to high-performance concurrent search structures. Similar techniques and frameworks can be applied to concurrent graph and network algorithms beyond search structures.
Statistics is Easy
Computational analysis of natural science experiments often confronts noisy data due to natural variability in environment or measurement. Drawing conclusions in the face of such noise entails a statistical analysis. Parametric statistical methods assume that the data is a sample from a population that can be characterized by a specific distribution (e.g., a normal distribution). When the assumption is true, parametric approaches can lead to high confidence predictions. However, in many cases particular distribution assumptions do not hold. In that case, assuming a distribution may yield false conclusions. The companion book Statistics is Easy! gave a (nearly) equation-free introduction to nonparametric (i.e., no distribution assumption) statistical methods. The present book applies data preparation, machine learning, and nonparametric statistics to three quite different life science datasets. We provide the code as applied to each dataset in both R and Python 3. We also include exercises for self-study or classroom use.
Statistics is Easy!
Statistics is the activity of inferring results about a population given a sample. Historically, statistics books assume an underlying distribution to the data (typically, the normal distribution) and derive results under that assumption. Unfortunately, in real life, one cannot normally be sure of the underlying distribution. For that reason, this book presents a distribution-independent approach to statistics based on a simple computational counting idea called resampling. This book explains the basic concepts of resampling, then systematically presents the standard statistical measures along with programs (in the language Python) to calculate them using resampling, and finally illustrates ...
Statistics is Easy! 2nd Edition
Statistics is the activity of inferring results about a population given a sample. Historically, statistics books assume an underlying distribution to the data (typically, the normal distribution) and derive results under that assumption. Unfortunately, in real life, one cannot normally be sure of the underlying distribution. For that reason, this book presents a distribution-independent approach to statistics based on a simple computational counting idea called resampling. This book explains the basic concepts of resampling, then system atically presents the standard statistical measures along with programs (in the language Python) to calculate them using resampling, and finally illustrates...
