Books: **75**

Cover | Title | Year |
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A practical guide to learning data structures simply and easily ABOUT THIS BOOK
* This book is a very practical, friendly, and useful guide that will help you analyze problems and choose the right data structures for your solution
* Learn to recognize data patterns for determining which structures apply to a given problem
* Explore the unique rules or "gotchas" more » that will help you become an excellent programmer
WHO THIS BOOK IS FOR
If you're self-taught programmers in any language who wants to gain a solid understanding of data structures and how to use them to solve real-world problems in your day-to-day development work, then this book is for you.
WHAT YOU WILL LEARN
* A rapid overview of data types, applications for each type, best practices and high-level variations between platforms
* Review the most common data structures and build working examples in the languages used for mobile platform software development
* Understand advanced data structure concepts such as generic collections, searching and sorting algorithms, and recursion
* Learn to use Stacks (LIFO) and queues (FIFO) in your daily application
* Add/remove objects and nest arrays and dictionaries within another dictionary and understand why such architecture is often preferred or necessary
* Get acquainted with the tree structures such as heap, binary, and graphs, apply them to work
* Unleash the power of different sorting techniques such as bubble sort, quick sort, merge sort, insertion sort, and radix sort
* Perform searching operations on arrays, heaps, graphs, and binary trees in different languages
* « less | 2017 | |

Learn functional data structures and algorithms for your applications and bring their benefits to your work now About This Book
Moving from object-oriented programming to functional programming? This book will help you get started with functional programming. Easy-to-understand explanations of practical topics will help you get started with functional data structures. Illustrative diagrams to explain the algorithms more » in detail. Get hands-on practice of Scala to get the most out of functional programming.
Who This Book Is
For This book is for those who have some experience in functional programming languages. The data structures in this book are primarily written in Scala, however implementing the algorithms in other functional languages should be straight forward.
What You Will Learn
* Learn to think in the functional paradigm
* Understand common data structures and the associated algorithms, as well as the context in which they are commonly used
* Take a look at the runtime and space complexities with the O notation
* See how ADTs are implemented in a functional setting
* Explore the basic theme of immutability and persistent data structures
* Find out how the internal algorithms are redesigned to exploit structural sharing, so that the persistent data structures perform well, avoiding needless copying. Get to know functional features like lazy evaluation and recursion used to implement efficient algorithms
* Gain Scala best practices and idioms
In Detail
Functional data structures have the power to improve the codebase of an application and improve efficiency. With the advent of functional programming and with powerful functional languages such as Scala, Clojure and Elixir becoming part of important enterprise applications, functional data structures have gained an important place in the developer toolkit. Immutability is a cornerstone of functional programming. Immutable and persistent data structures are thread safe by « less | 2017 | |

A Practical Guide Creating robust software requires the use of efficient algorithms, but programmers seldom think about them until a problem occurs. This updated edition of Algorithms in a Nutshell describes a large number of existing algorithms for solving a variety of problems, and helps you select and implement the more » right algorithm for your needs—with just enough math to let you understand and analyze algorithm performance.
With its focus on application, rather than theory, this book provides efficient code solutions in several programming languages that you can easily adapt to a specific project. Each major algorithm is presented in the style of a design pattern that includes information to help you understand why and when the algorithm is appropriate.
With this book, you will:
* Solve a particular coding problem or improve on the performance of an existing solution
* Quickly locate algorithms that relate to the problems you want to solve, and determine why a particular algorithm is the right one to use
* Get algorithmic solutions in C, C++, Java, and Ruby with implementation tips
* Learn the expected performance of an algorithm, and the conditions it needs to perform at its best
* Discover the impact that similar design decisions have on different algorithms
* Learn advanced data structures to improve the efficiency of algorithms « less | 2016 | |

Data Structures and Algorithmic Puzzles Table of Contents: goo.gl/JFMgiUSample Chapter: goo.gl/n2Hk4iFound Issue? goo.gl/forms/4Gt72YO81IVideos: goo.gl/BcHq74
"Data Structures And Algorithms Made Easy: Data Structures and Algorithmic Puzzles" is a book that offers solutions to complex data structures and algorithms. There are multiple solutions more » for each problem and the book is coded in C/C++, it comes handy as an interview and exam guide for computer scientists. It can be used as a reference manual by those readers in the computer science industry. This book serves as guide to prepare for interviews, exams, and campus work. In short, this book offers solutions to various complex data structures and algorithmic problems.
Topics Covered:
1. Introduction
2. Recursion and Backtracking
3. Linked Lists
4. Stacks
5. Queues
6. Trees
7. Priority Queue and Heaps
8. Disjoint Sets ADT
9. Graph Algorithms
10. Sorting
11. Searching
12. Selection Algorithms [Medians]
13. Symbol Tables
14. Hashing
15. String Algorithms
16. Algorithms Design Techniques
17. Greedy Algorithms
18. Divide and Conquer Algorithms
19. Dynamic Programming
20. Complexity Classes
21. Miscellaneous Concepts « less | 2016 | |

This dynamic reference work provides solutions to vital algorithmic problems for scholars, researchers, practitioners, teachers and students in fields such as computer science, mathematics, statistics, biology, economics, financial software, and medical informatics.
This second edition is broadly more » expanded, building upon the success of its former edition with more than 450 new and updated entries. These entries are designed to ensure algorithms are presented from growing areas of research such as bioinformatics, combinatorial group testing, differential privacy, enumeration algorithms, game theory, massive data algorithms, modern learning theory, social networks, and VLSI CAD algorithms.
Over 630 entries are organized alphabetically by problem, with subentries allowing for distinct solutions. Each entry includes a description of the basic algorithmic problem; the input and output specifications; key results; examples of applications; citations to key literature, open problems, experimental results, links to data sets and downloadable code.
All entries are peer-reviewed, written by leading experts in the field―and each entry contains links to a summary of the author’s research work.
This defining reference is available in both print and online―a dynamic living work with hyperlinks to related entries, cross references citations, and a myriad other valuable URLs.
New and Updated entries include:
Algorithmic Aspects of Distributed Sensor Networks,
Algorithms for Modern Computers
Bioinformatics
Certified Reconstruction and Mesh Generation
Combinatorial Group Testing
Compression of Text and Data Structures
Computational Counting
Computational Economics
Computational Geometry
Differential Privacy
Enumeration Algorithms
Exact Exponential Algorithms
Game Theory
Graph Drawing
Group Testing
Internet Algorithms
Kernels and Compressions
Massive Data Algorithms
Mathematical Optimization
Modern Learning Theory
Social Networks
Stable Marriage Problems, k-SAT Algorithms
Sublinear Algorithms
Tile Self-Assembly
VLSI CAD Algorithms « less | 2016 | |

Maximize your NLP capabilities while creating amazing NLP projects in Python ABOUT THIS BOOK
* Learn to implement various NLP tasks in Python
* Gain insights into the current and budding research topics of NLP
* This is a comprehensive step-by-step guide to help students and researchers create their own projects based on real-life applications
WHO THIS BOOK IS FOR
This more » book is for intermediate level developers in NLP with a reasonable knowledge level and understanding of Python.
WHAT YOU WILL LEARN
* Implement string matching algorithms and normalization techniques
* Implement statistical language modeling techniques
* Get an insight into developing a stemmer, lemmatizer, morphological analyzer, and morphological generator
* Develop a search engine and implement POS tagging concepts and statistical modeling concepts involving the n gram approach
* Familiarize yourself with concepts such as the Treebank construct, CFG construction, the CYK Chart Parsing algorithm, and the Earley Chart Parsing algorithm
* Develop an NER-based system and understand and apply the concepts of sentiment analysis
* Understand and implement the concepts of Information Retrieval and text summarization
* Develop a Discourse Analysis System and Anaphora Resolution based system
IN DETAIL
Natural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning.
This book will give you expertise on how to employ various NLP tasks in Python, giving you an insight into the best practices when designing and building NLP-based applications using Python. It will help you become an expert in no time and assist you in creating your own NLP projects using NLTK.
You will sequentially be guided through applying machine learning tools to develop various models. We'll give you clarity on how to create training data and how to implement major NLP applications such as Named Entity Recognition, Question Answering System, Discourse Analysis, Transliteration, Word Sense disambiguation, Information Retrieval, Sentiment Analysis, Text Summarization, and Anaphora Resolution.
STYLE AND APPROACH
This is an easy-to-follow guide, full of hands-on examples of real-world tasks. Each topic is explained and placed in context, and for the more inquisitive, there are more details of the concepts used. « less | 2016 | |

The tool for visualization is Microsoft Visual C++. This popular software has the standard C++ combined with the Microsoft Foundation Classes (MFC) libraries for Windows visualization. This book explains how to create a graph interactively, solve problems in graph theory with minimum number of C++ codes, more » and provide friendly interfaces that makes learning the topics an interesting one. Each topic in the book comes with working Visual C++ codes which can easily be adapted as solutions to various problems in science and engineering. « less | 2016 | |

Design and Analysis Algorithms: Design and Analysis of is a textbook designed for the undergraduate and postgraduate students of computer science engineering, information technology, and computer applications. It helps the students to understand the fundamentals and applications of algorithms. The book has been divided more » into four sections: Algorithm Basics, Data Structures, Design Techniques and Advanced Topics. The first section explains the importance of algorithms, growth of functions, recursion and analysis of algorithms. The second section covers the data structures basics, trees, graphs, sorting in linear and quadratic time. Section three discusses the various design techniques namely, divide and conquer, greedy approach, dynamic approach, backtracking, branch and bound and randomized algorithms used for solving problems in separate chapters. The fourth section includes the advanced topics such as transform and conquer, decrease and conquer, number thoeretics, string matching, computational geometry, complexity classes, approximation algorithms, and parallel algorithms. Finally, the applications of algorithms in Machine Learning and Computational Biology areas are dealt with in the subsequent chapters. This section will be useful for those interested in advanced courses in algorithms. The book also has 10 appendixes which include topics like probability, matrix operations, Red-black tress, linear programming, DFT, scheduling, a reprise of sorting, searching and amortized analysis and problems based on writing algorithms. The concepts and algorithms in the book are explained with the help of examples which are solved using one or more methods for better understanding. The book includes variety of chapter-end pedagogical features such as point-wise summary, glossary, multiple choice questions with answers, review questions, application-based exercises to help readers test their understanding of the learnt concepts. « less | 2015 | |

Data Structure and Algorithmic Puzzles PeelingData Structures and Algorithms[re-printed on 21-January-2016]:Table of Contents: goo.gl/VLEUcaSample Chapter: goo.gl/8AEcYkSource Code: goo.gl/L8XxdtErrata: goo.gl/EVftlsFound issue?goo.gl/forms/uLXGYpyuzXVideos: goo.gl/BcHq74
The sample chapter should give you a very good idea of the quality more » and style of our book. In particular, be sure you are comfortable with the level and with our Python coding style. This book focuses on giving solutions for complex problems in data structures and algorithm. It even provides multiple solutions for a single problem, thus familiarizing readers with different possible approaches to the same problem.
"Data Structure and Algorithmic Thinking with Python" is designed to give a jump-start to programmers, job hunters and those who are appearing for exams. All the code in this book are written in Python. It contains many programming puzzles that not only encourage analytical thinking, but also prepares readers for interviews. This book, with its focused and practical approach, can help readers quickly pick up the concepts and techniques for developing efficient and effective solutions to problems.
Topics Covered:
* Organization of chapters
* Introduction
* Recursion and Backtracking
* Linked Lists
* Stacks
* Queues
* Trees
* Priority Queue and Heaps
* Disjoint Sets ADT
* Graph Algorithms
* Sorting
* Searching
* Selection Algorithms [Medians]
* Symbol Tables
* Hashing
* String Algorithms
* Algorithms Design Techniques
* Greedy Algorithms
* Divide and Conquer Algorithms
* Dynamic Programming
* Complexity Classes
* Miscellaneous Concepts « less | 2015 | |

Offers a comprehensive introduction to the fundamental structures and applications of a wide range of contemporary coding operations
This book offers a comprehensive introduction to the fundamental structures and applications of a wide range of contemporary coding operations. This text focuses on the more » ways to structure information so that its transmission will be in the safest, quickest, and most efficient and error-free manner possible. All coding operations are covered in a single framework, with initial chapters addressing early mathematical models and algorithmic developments which led to the structure of code. After discussing the general foundations of code, chapters proceed to cover individual topics such as notions of compression, cryptography, detection, and correction codes. Both classical coding theories and the most cutting-edge models are addressed, along with helpful exercises of varying complexities to enhance comprehension.
* Explains how to structure coding information so that its transmission is safe, error-free, efficient, and fast
* Includes a pseudo-code that readers may implement in their preferential programming language
* Features descriptive diagrams and illustrations, and almost 150 exercises, with corrections, of varying complexity to enhance comprehension
Foundations of Coding: Compression, Encryption,Error-Correction is an invaluable resource for understanding the various ways information is structured for its secure and reliable transmission in the 21st-century world. « less | 2015 |