Narasimha Karumanchi

<p><strong>Peeling Data Structures and Algorithms: </strong>

内容紹介

Narasimha Karumanchi is the founder of CareerMonk Publications and author of few books on data structures, algorithms, and design patterns. He was a software developer who has been both interviewer and interviewee over his long career. Most recently he worked for Amazon Corporation, IBM Software Labs, Mentor Graphics, and Microsoft. Narasimha holds an M.Tech. in computer science from IIT, Bombay, and B.Tech. from JNT university. He authored the following books which got translated to international languages: Chinese, Japanese, Korea and Taiwan. Also, around 58 international universities were using these books as reference for academic courses.

著者について

Peeling Data Structures and Algorithms for interviews [re-printed with corrections and new problems]: "Data Structures And Algorithms Made Easy: Data Structure And Algorithmic Puzzles" is a book that offers solutions to complex data structures and algorithms. There are multiple solutions for each problem and the book is coded in C/C++, it comes handy as an interview and exam guide for computer scientists. A handy guide of sorts for any computer science professional, "Data Structures And Algorithms Made Easy: Data Structure And Algorithmic Puzzles" is a solution bank for various complex problems related to data structures and algorithms. It can be used as a reference manual by those readers in the computer science industry. The book has around 21 chapters and covers 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, and other Miscellaneous Concepts. Data Structures And Algorithms Made Easy: Data Structure And Algorithmic Puzzles by Narasimha Karumanchi was published in March, and it is coded in C/C++ language. This book serves as guide to prepare for interviews, exams, and campus work. It is also available in Java. In short, this book offers solutions to various complex data structures and algorithmic problems. What is unique? Our main objective isn't to propose theorems and proofs about DS and Algorithms. We took the direct route and solved problems of varying complexities. That is, each problem corresponds to multiple solutions with different complexities. In other words, we enumerated possible solutions. With this approach, even when a new question arises, we offer a choice of different solution strategies based on your priorities. Topics Covered: IntroductionRecursion and BacktrackingLinked ListsStacksQueuesTreesPriority Queue and HeapsDisjoint Sets ADTGraph AlgorithmsSorting Searching Selection Algorithms [Medians] Symbol Tables Hashing String Algorithms Algorithms Design Techniques Greedy Algorithms Divide and Conquer Algorithms Dynamic Programming Complexity Classes Miscellaneous Concepts Target Audience? These books prepare readers for interviews, exams, and campus work. Language? All code was written in C/C++. If you are using Java, please search for "Data Structures and Algorithms Made Easy in Java." Also, check out sample chapters and the blog at: CareerMonk.com

内容詳細

Data Structures And Algorithms Made Easy: Data Structure And Algorithmic Puzzles is a book that offers solutions to complex data structures and algorithms. There are multiple solutions for each problem and the book is coded in C/C++, it comes handy as an interview and exam guide for computer...

The objective of this book is to present the ideas for solving data-structure and algorithmic problems to prepare readers for interviews, exams, and academic work.

Data Structures and Algorithms Made Easy

Mark Newman

This book explains the fundamentals of computational physics and describes the techniques that every physicist should know, such as finite difference methods, numerical quadrature, and the fast Fourier transform. The book offers a complete introduction to the topic at the undergraduate level, and is also suitable for the advanced student or researcher. The book begins with an introduction to Python, then moves on to a step-by-step description of the techniques of computational physics, with examples ranging from simple mechanics problems to complex calculations in quantum mechanics, electromagnetism, statistical mechanics, and more.

Computational Physics

Timothy Masters

Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. The first of three in a series on C++ and CUDA C deep learning and belief nets, Deep Belief Nets in C++ and CUDA C: Volume 1 shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a thought process that is capable of learning abstract concepts built from simpler primitives. As such, you’ll see that a typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. All the routines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines. What You Will Learn Employ deep learning using C++ and CUDA C Work with supervised feedforward networks Implement restricted Boltzmann machines Use generative samplings Discover why these are important Who This Book Is For Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.

News flash... If anyone would prefer reading these books in Korean, Volume 1 is now available from a South Korean publisher, with Volumes 2 and 3 available soon: http://www.acornpub.co.kr/book/dbn-cuda-vol1Deep belief nets are one of the most exciting recent developments in artificial intelligence. The structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a thought process that is capable of learning abstract concepts built from simpler primitives. A typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting.This book presents the essential building blocks of the most common forms of deep belief nets. At each step the text provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards.Source code for all routines presented in the book, and the DEEP program which implements these algorithms, are available for free download from the authors website.NOTE... The source code available for free download includes all of the code listed in the book, along with some libraries of related routines. Complete code for the DEEP program is not included; this code is enormous, as it includes many Windows-only interface routines, screen display code, and so forth. Users who wish to write their own DBN programs are responsible for implementing their own hardware/OS interface, while using my supplied code for the mathematical calculations.

Timothy Masters received a PhD in mathematical statistics with a specialization in numerical computing. Since then he has continuously worked as an independent consultant for government and industry. His early research involved automated feature detection in high-altitude photographs while he developed applications for flood and drought prediction, detection of hidden missile silos, and identification of threatening military vehicles. Later he worked with medical researchers in the development of computer algorithms for distinguishing between benign and malignant cells in needle biopsies. For the last twenty years he has focused primarily on methods for evaluating automated financial market trading systems. He has authored five books on practical applications of predictive modeling: Practical Neural Network Recipes in C]+ (Academic Press, 1993) Signal and Image Processing with Neural Networks (Wiley, 1994) Advanced Algorithms for Neural Networks (Wiley, 1995) Neural, Novel, and Hybrid Algorithms for Time Series Prediction (Wiley, 1995) Assessing and Improving Prediction and Classification (CreateSpace, 2013)

出版社：Createspace Independent Pubの検索結果

Data Structures and Algorithms Made Easy

Computational Physics

Deep Belief Nets in C++ and Cuda C: Restricted Boltzmann Machines and Supervised Feedforward Networks