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  • Book


  • Authors: Dimitri Bertsekas (1992)

  • This volume is designed to help professionals develop a deeper understanding of data networks and evolving integrated networks, and to explore today's various analysis and design tools.

  • Book


  • Authors: Steven Halim (2020)

  • This Competitive Programming book, 4th edition (CP4) is a must have for every competitive programmer. Mastering the contents of this book is a necessary (but admittedly not sufficient) condition if one wishes to take a leap forward from being just another ordinary coder to being among one of the world's finest competitive programmers.

  • Book


  • Authors: Steven Halim (2020)

  • This Competitive Programming book, 4th edition (CP4) is a must have for every competitive programmer. Mastering the contents of this book is a necessary (but admittedly not sufficient) condition if one wishes to take a leap forward from being just another ordinary coder to being among one of the world's finest competitive programmers.

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  • Authors: Kevin P. Murphy (2012)

  • The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics.

  • Book


  • Authors: Ethem Alpaydin (2014)

  • Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods.

  • Book


  • Authors: Michael Sipser (2012)

  • The number one choice for today's computational theory course, this revision continues the book's well-known, approachable style with timely revisions, additional practice, and more memorable examples in key areas. A new first-of-its-kind theoretical treatment of deterministic context-free languages is ideal for a better understanding of parsing and LR grammars.

  • Book


  • Authors: Sebastian Raschka (2015)

  • Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization * Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms * Ask - and answer - tough questions of your data with robust statistical models, built for a range of datasets

  • Book


  • Authors: Abraham Silberschatz (2018)

  • It combines instruction on concepts with real-world applications so that students can understand the practical usage of the content. End-of-chapter problems, exercises, review questions, and programming exercises help to further reinforce important concepts. New interactive self-assessment problems are provided throughout the text to help students monitor their level of understanding and progress.