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Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. Summary Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. You’ll wor Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. Summary Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. You’ll work through a series of exercises based in computer science fundamentals that are designed to improve your software development abilities, improve your understanding of artificial intelligence, and even prepare you to ace an interview. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Whatever software development problem you’re facing, odds are someone has already uncovered a solution. This book collects the most useful solutions devised, guiding you through a variety of challenges and tried-and-true problem-solving techniques. The principles and algorithms presented here are guaranteed to save you countless hours in project after project. About the book Classic Computer Science Problems in Java is a master class in computer programming designed around 55 exercises that have been used in computer science classrooms for years. You’ll work through hands-on examples as you explore core algorithms, constraint problems, AI applications, and much more. What's inside     Recursion, memoization, and bit manipulation     Search, graph, and genetic algorithms     Constraint-satisfaction problems     K-means clustering, neural networks, and adversarial search About the reader For intermediate Java programmers. About the author David Kopec is an assistant professor of Computer Science and Innovation at Champlain College in Burlington, Vermont. Table of Contents 1 Small problems 2 Search problems 3 Constraint-satisfaction problems 4 Graph problems 5 Genetic algorithms 6 K-means clustering 7 Fairly simple neural networks 8 Adversarial search 9 Miscellaneous problems 10 Interview with Brian Goetz


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Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. Summary Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. You’ll wor Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. Summary Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. You’ll work through a series of exercises based in computer science fundamentals that are designed to improve your software development abilities, improve your understanding of artificial intelligence, and even prepare you to ace an interview. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Whatever software development problem you’re facing, odds are someone has already uncovered a solution. This book collects the most useful solutions devised, guiding you through a variety of challenges and tried-and-true problem-solving techniques. The principles and algorithms presented here are guaranteed to save you countless hours in project after project. About the book Classic Computer Science Problems in Java is a master class in computer programming designed around 55 exercises that have been used in computer science classrooms for years. You’ll work through hands-on examples as you explore core algorithms, constraint problems, AI applications, and much more. What's inside     Recursion, memoization, and bit manipulation     Search, graph, and genetic algorithms     Constraint-satisfaction problems     K-means clustering, neural networks, and adversarial search About the reader For intermediate Java programmers. About the author David Kopec is an assistant professor of Computer Science and Innovation at Champlain College in Burlington, Vermont. Table of Contents 1 Small problems 2 Search problems 3 Constraint-satisfaction problems 4 Graph problems 5 Genetic algorithms 6 K-means clustering 7 Fairly simple neural networks 8 Adversarial search 9 Miscellaneous problems 10 Interview with Brian Goetz

28 review for Classic Computer Science Problems in Java

  1. 4 out of 5

    Xanan

    Reading this book will do no harm but ... The book discusses several algorithmic problems and illustrates how to solve them with Java. The code is reasonably simple to understand, well explained, and makes no use of external libraries. The author avoids math theory and formulas, making the book easy to follow for those who lack a mathematical background. This book is not addressed to Java beginners and is not a book on algorithms that explains Java along the way. Readers are supposed to already know Reading this book will do no harm but ... The book discusses several algorithmic problems and illustrates how to solve them with Java. The code is reasonably simple to understand, well explained, and makes no use of external libraries. The author avoids math theory and formulas, making the book easy to follow for those who lack a mathematical background. This book is not addressed to Java beginners and is not a book on algorithms that explains Java along the way. Readers are supposed to already know Java 8 (lambdas, streams and functional interfaces are used extensively besides collections and generics). Proposed solutions are often defined in terms of generic data types in order to make them applicable to a wider context than the motivational example. Almost always though, the proposed solutions rely on several simplifying assumptions or disregard details that may be important in a production environment. The book describes the basics of how to solve some classic problems in Java, so you can use is as a starting point to elaborate something more concrete that can be used in a real setting. However, for several problems discussed in the book, libraries exists that tackle that specific problem (the author notes that too). For instance, if you have to program a neural network, you will probably have to know some math (which the book does not provide), you will have to face some complexities that are not addressed in the book and, most important, rather than implement your own code you will want to rely on a library that was specifically developed for neural network programming, has been thoroughly tested and is widely accepted. More specific books that describe neural networks and the library are a better solution. This is true for other concepts discussed in the book, including graph algorithms, genetic algorithms, machine learning. The final chapter reports an interview by the author with Brian Goetz on his work as a Java architect. Sure, Goetz is a great Java guru and I greatly appreciated his concurrency book, but I did not expect to pay to read the transcript of one of his interviews. Those 14 pages could have been used for more on Java algorithms.

  2. 5 out of 5

    David Kopec

  3. 4 out of 5

    Dowlath

  4. 5 out of 5

    César Laurenti

  5. 4 out of 5

    Derik

  6. 5 out of 5

    Miguel Paraz

  7. 4 out of 5

    Renanreismartins

  8. 4 out of 5

    Chukson

  9. 4 out of 5

    Petar

  10. 4 out of 5

    Svetlana

  11. 4 out of 5

    Łukasz Słonina

  12. 5 out of 5

    Karlo Novak

  13. 4 out of 5

    Brus

  14. 5 out of 5

    Nikola

  15. 5 out of 5

    Túlio Ribeiro dos Anjos

  16. 5 out of 5

    Eduard Kouts

  17. 5 out of 5

    Umut Salih

  18. 5 out of 5

    Martin

  19. 4 out of 5

    Onur Yurtsever

  20. 5 out of 5

    Byron Samson

  21. 5 out of 5

    Fahed Mohamedi

  22. 4 out of 5

    Tayná

  23. 4 out of 5

    Eddy

  24. 5 out of 5

    Scavazzini

  25. 5 out of 5

    Christopher Jaime

  26. 5 out of 5

    Darryn Jones

  27. 4 out of 5

    Balamurugan Krishnamoorthy

  28. 4 out of 5

    Nikolay Rybak

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