Publisher: | Apress |

Series: | Apress , Mastering |

Author: | Magnus Lie Hetland |

Edition: | 1 |

ISBN-10: | 1430232374 |

ISBN-13: | 9781430232377 |

Pages: | 336 |

Published: | Nov 24 2010 |

Posted: | Nov 19 2014 |

Language: | English |

Book format: | |

Book size: | 3.21 MB |

Python Algorithms explains the Python approach to algorithm analysis and design. Written by Magnus Lie Hetland, author of Beginning Python,this bookis sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques. The book deals with some of the most important and challenging areas of programming and computer science, but in a highly pedagogic and readable manner. The book covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others himself. What youll learn Transform new problems to well-known algorithmic problems with efficient solutions, or show that the problems belong to classes of problems thought not to be efficiently solvable. Analyze algorithms and Python programs both using mathematical tools and basic experiments and benchmarks. Prove correctness, optimality, or bounds on approximation error for Python programs and their underlying algorithms. Understand several classical algorithms and data structures in depth, and be able to implement these efficiently in Python. Design and implement new algorithms for new problems, using time-tested design principles and techniques. Speed up implementations, using a plethora of tools for high-performance computing in Python. Who this book is for The book is intended for Python programmers who need to learn about algorithmic problem-solving, or who need a refresher. Students of computer science, or similar programming-related topics, such as bioinformatics, may also find the book to be quite useful. Table of Contents Introduction The Basics Counting 101 Induction and Recursion ... and Reduction Traversal: The Skeleton Key of Algorithmics Divide, Combine, and Conquer Greed Is Good? Prove It! Tangled Dependencies and Memoization From A to B with Edsger and Friends Matchings, Cuts, and Flows Hard Problems and (Limited) Sloppiness

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2nd Edition

Python Algorithms, Second Edition explains the Python approach to algorithm analysis and design. Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques. The book deals with some of the most important and challenging areas of programming and computer science in a highly readable manner. It covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, a...

If you want a basic understanding of computer visionamp;#8217;s underlying theory and algorithms, this hands-on introduction is the ideal place to start. Youamp;#8217;ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python. Programming Computer Vision with Python explains computer vision in broad terms that wonamp;#8217;t bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what youamp;#8217;ve learned. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills....

This textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. Features: includes both introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses provided in the preface; provides learning goals, review questions and programming exercises in each cha...

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