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en-caRss Generator By insigniasoftware.comComputer organization and embedded systems
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Hamacher, V. Carl.
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<p> The sixth edition of this book covers the key topics in computer organization and embedded systems. It presents hardware design principles and shows how hardware design is influenced by the requirements of software. The book carefully explains the main principles supported by examples drawn from commercially available processors.
The book is suitable for undergraduate electrical and computer engineering majors and computer science specialists. It is intended for a first course in computer organization and embedded systems. </p>
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<p>Date Published:2012</p>
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</table>Sunan-e-Nisai
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Abbu Abdur Rahman Ahmad Bin Shoaib Bin Ali Nisai
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<p>Date Published:2018</p>
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</table>Sunan-e-Nisai
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Abbu Abdur Rahman Ahmad Bin Shoaib Bin Ali Nisai
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<p>Date Published:2018</p>
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</table>Quantum computing for computer scientists
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Yanofsky, Noson S., 1967-
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<p> The multidisciplinary field of quantum computing strives to exploit some of the uncanny aspects of quantum mechanics to expand our computational horizons. Quantum Computing for Computer Scientists takes readers on a tour of this fascinating area of cutting-edge research. Written in an accessible yet rigorous fashion, this book employs ideas and techniques familiar to every student of computer science. The reader is not expected to have any advanced mathematics or physics background. After presenting the necessary prerequisites, the material is organized to look at different aspects of quantum computing from the specific standpoint of computer science. There are chapters on computer architecture, algorithms, programming languages, theoretical computer science, cryptography, information theory, and hardware. The text has step-by-step examples, more than two hundred exercises with solutions, and programming drills that bring the ideas of quantum computing alive for today's computer science students and researchers. </p>
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<p>Date Published:2008</p>
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</table>Sunan-e-Nisai
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Abbu Abdur Rahman Ahmad Bin Shoaib Bin Ali Nisai
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<p>Date Published:2018</p>
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</table>Principles of engineering thermodynamics
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Moran, Michael J.,
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<p> Includes index. Now in its Eighth Edition, Principles of Engineering Thermodynamics continues to set the standard for teaching readers how to be effective problem solvers, emphasizing the authors? signature methodologies that have taught over a half million students worldwide. This new edition provides a student-friendly approach that emphasizes the relevance of thermodynamics principles to some of the most critical issues of today and coming decades, including a wealth of integrated coverage of energy and the environment, biomedical/bioengineering, as well as emerging technologies. Visualization skills are developed and basic principles demonstrated through a complete set of animations that have been interwoven throughout. This edition also introduces co-authors Daisie Boettner and Margaret Bailey, who bring their rich backgrounds of success in teaching and research in thermodynamics to the text. </p>
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<p>Date Published:2015</p>
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</table>Probability and random processes for electrical engineering
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Leon-Garcia, Alberto
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<p> Includes index Probability models in electrical and computer engineering -- Basic concepts of probability theory -- Random variables -- Multiple random variables -- Sums of random variables and long-term averages -- Random processes -- Analysis and processing of random signals -- Markov chains -- Introduction to queueing theory. This is the standard textbook for courses on probability and statistics, not substantially updated. While helping students to develop their problem-solving skills, the author motivates students with practical applications from various areas of ECE that demonstrate the relevance of probability theory to engineering practice. Included are chapter overviews, summaries, checklists of important terms, annotated references, and a wide selection of fully worked-out real-world examples. In this edition, the Computer Methods sections have been updated and substantially enhanced and new problems have been added. </p>
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<p>Date Published:1994</p>
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</table>Problems in operations research : principles and solutions
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Gupta, Prem Kumar Er., 1933-
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<p> For Engineering, Computer Science, Commerce and Management, Economics, Statistics, Mathematics, C.A., I.C.W.A.,C.S. Also useful for I.A.S. and other Competitive Examinations. Many new exercises from the latest examination papers have been included along with hints for all difficult exercises. The book now covers questions up to 2008 examinations. </p>
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<p>Date Published:2018</p>
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</table>Python deep learning : exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow
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Vasilev, Ivan,
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<p> Includes Index Machine Learning – An Introduction
Neural Networks
Deep Learning Fundamentals
Computer Vision With Convolutional Networks
Advanced Computer Vision
Generating images with GANs and Variational Autoencoders
Recurrent Neural Networks and Language Models
Reinforcement Learning Theory
Deep Reinforcement Learning for Games
Deep Learning in Autonomous Vehicles. With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects.
This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota.
By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.
What you will learn
Grasp the mathematical theory behind neural networks and deep learning processes
Investigate and resolve computer vision challenges using convolutional networks and capsule networks
Solve generative tasks using variational autoencoders and Generative Adversarial Networks
Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models
Explore reinforcement learning and understand how agents behave in a complex environment
Get up to date with applications of deep learning in autonomous vehicles
Who this book is for
This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book. </p>
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<p>Date Published:2019</p>
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</table>Python natural language processing : explore NLP with machine learning and deep learning techniques
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Thanaki, Jalaj
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<p> Includes Index Introduction
Practical Understanding of a Corpus and Dataset
Understanding the Structure of a Sentences
Preprocessing
Feature Engineering and NLP Algorithms
Advanced Feature Engineering and NLP Algorithms
Rule-Based System for NLP
Machine Learning for NLP Problems
Deep Learning for NLU and NLG Problems
Advanced Tools
How to Improve Your NLP Skills
Installation Guide. This book starts off by laying the foundation for Natural Language Processing and why Python is one of the best options to build an NLP-based expert system with advantages such as Community support, availability of frameworks and so on. Later it gives you a better understanding of available free forms of corpus and different types of dataset. After this, you will know how to choose a dataset for natural language processing applications and find the right NLP techniques to process sentences in datasets and understand their structure. You will also learn how to tokenize different parts of sentences and ways to analyze them.
During the course of the book, you will explore the semantic as well as syntactic analysis of text. You will understand how to solve various ambiguities in processing human language and will come across various scenarios while performing text analysis.
You will learn the very basics of getting the environment ready for natural language processing, move on to the initial setup, and then quickly understand sentences and language parts. You will learn the power of Machine Learning and Deep Learning to extract information from text data.
By the end of the book, you will have a clear understanding of natural language processing and will have worked on multiple examples that implement NLP in the real world.
What you will learn
Focus on Python programming paradigms, which are used to develop NLP applications
Understand corpus analysis and different types of data attribute.
Learn NLP using Python libraries such as NLTK, Polyglot, SpaCy, Standford CoreNLP and so on
Learn about Features Extraction and Feature selection as part of Features Engineering.
Explore the advantages of vectorization in Deep Learning.
Get a better understanding of the architecture of a rule-based system.
Optimize and fine-tune Supervised and Unsupervised Machine Learning algorithms for NLP problems.
Identify Deep Learning techniques for Natural Language Processing and Natural Language Generation problems. </p>
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<p>Date Published:2017</p>
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</table>R for Everyone : Advanced Analytics and Graphics
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Lander, Jared P
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<p> 1. Getting R -- 1.1. Downloading R -- 1.2.R Version -- 1.3.32-bit versus 64-bit -- 1.4. Installing -- 1.5. Revolution R Community Edition -- 1.6. Conclusion -- 2. The R Environment -- 2.1.Command Line Interface -- 2.2. RStudio -- 2.3. Revolution Analytics RPE -- 2.4. Conclusion -- 3.R Packages -- 3.1. Installing Packages -- 3.2. Loading Packages -- 3.3. Building a Package -- 3.4. Conclusion -- 4. Basics of R -- 4.1. Basic Math -- 4.2. Variables -- 4.3. Data Types -- 4.4. Vectors -- 4.5. Calling Functions -- 4.6. Function Documentation -- 4.7. Missing Data -- 4.8. Conclusion -- 5. Advanced Data Structures -- 5.1.data.frames -- 5.2. Lists -- 5.3. Matrices -- 5.4. Arrays -- 5.5. Conclusion -- 6. Reading Data into R -- 6.1. Reading CSVs -- 6.2. Excel Data -- 6.3. Reading from Databases -- 6.4. Data from Other Statistical Tools -- 6.5.R Binary Files -- 6.6. Data Included with R -- 6.7. Extract Data from Web Sites -- 6.8. Conclusion -- 7. Statistical Graphics -- 7.1. Base Graphics -- 7.2.ggplot2 -- 7.3. Conclusion -- 8. Writing R Functions -- 8.1. Hello, World! -- 8.2. Function Arguments -- 8.3. Return Values -- 8.4.do. call -- 8.5. Conclusion -- 9. Control Statements -- 9.1. If and else -- 9.2.switch -- 9.3.ifelse -- 9.4.Compound Tests -- 9.5. Conclusion -- 10. Loops, the Un-R Way to Iterate -- 10.1. For Loops -- 10.2. While Loops -- 10.3. Controlling Loops -- 10.4. Conclusion -- 11. Group Manipulation -- 11.1. Apply Family -- 11.2.aggregate -- 11.3.plyr -- 11.4.data.table -- 11.5. Conclusion -- 12. Data Reshaping -- 12.1.cbind and rbind -- 12.2. Joins -- 12.3.reshape2 -- 12.4. Conclusion -- 13. Manipulating Strings -- 13.1.paste -- 13.2.sprintf -- 13.3. Extracting Text -- 13.4. Regular Expressions -- 13.5. Conclusion -- 14. Probability Distributions -- 14.1. Normal Distribution -- 14.2. Binomial Distribution -- 14.3. Poisson Distribution -- 14.4. Other Distributions -- 14.5. Conclusion -- 15. Basic Statistics -- 15.1. Summary Statistics -- 15.2. Correlation and Covariance -- 15.3.T-Tests -- 15.4. ANOVA -- 15.5. Conclusion -- 16. Linear Models -- 16.1. Simple Linear Regression -- 16.2. Multiple Regression -- 16.3. Conclusion -- 17. Generalized Linear Models -- 17.1. Logistic Regression -- 17.2. Poisson Regression -- 17.3. Other Generalized Linear Models -- 17.4. Survival Analysis -- 17.5. Conclusion -- 18. Model Diagnostics -- 18.1. Residuals -- 18.2.Comparing Models -- 18.3. Cross-Validation -- 18.4. Bootstrap -- 18.5. Stepwise Variable Selection -- 18.6. Conclusion -- 19. Regularization and Shrinkage -- 19.1. Elastic Net -- 19.2. Bayesian Shrinkage -- 19.3. Conclusion -- 20. Nonlinear Models -- 20.1. Nonlinear Least Squares -- 20.2. Splines -- 20.3. Generalized Additive Models -- 20.4. Decision Trees -- 20.5. Random Forests -- 20.6. Conclusion -- 21. Time Series and Autocorrelation -- 21.1. Autoregressive Moving Average -- 21.2. VAR -- 21.3. GARCH -- 21.4. Conclusion -- 22. Clustering -- 22.1.K-means -- 22.2. PAM -- 22.3. Hierarchical Clustering -- 22.4. Conclusion -- 23. Reproducibility, Reports and Slide Shows with knitr -- 23.1. Installing LATEX Program -- 23.2. LATEX Primer -- 23.3. Using knitr with LATEX -- 23.4. Markdown Tips -- 23.5. Using knitr and Markdown -- 23.6.pandoc -- 23.7. Conclusion -- 24. Building R Packages -- 24.1. Folder Structure -- 24.2. Package Files -- 24.3. Package Documentation -- 24.4. Checking, Building and Installing -- 24.5. Submitting to CRAN -- 24.6.C++ Code -- 24.7. Conclusion -- A. Real-Life Resources -- A.1. Meetups -- A.2. Stackoverflow -- A.3. Twitter -- A.4. Conferences -- A.5. Web Sites -- A.6. Documents -- A.7. Books -- A.8. Conclusion -- B. Glossary. Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution.
Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks.
Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques.
By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most.
COVERAGE INCLUDES
• Exploring R, RStudio, and R packages
• Using R for math: variable types, vectors, calling functions, and more
• Exploiting data structures, including data.frames, matrices, and lists
• Creating attractive, intuitive statistical graphics
• Writing user-defined functions
• Controlling program flow with if, ifelse, and complex checks
• Improving program efficiency with group manipulations
• Combining and reshaping multiple datasets
• Manipulating strings using R’s facilities and regular expressions
• Creating normal, binomial, and Poisson probability distributions
• Programming basic statistics: mean, standard deviation, and t-tests
• Building linear, generalized linear, and nonlinear models
• Assessing the quality of models and variable selection
• Preventing overfitting, using the Elastic Net and Bayesian methods
• Analyzing univariate and multivariate time series data
• Grouping data via K-means and hierarchical clustering
• Preparing reports, slideshows, and web pages with knitr
• Building reusable R packages with devtools and Rcpp
• Getting involved with the R global community. </p>
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<p>Date Published:2018</p>
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</table>Statistical programing in SAS
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Bailer, A. John,
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<p> 1. Structuring, implementing, and debugging programs to learn about data
Statistical Programming
Learning from Constructed, Artificial Data
Good Programming Practice
SAS Program Structure
What Is a SAS Data Set?
Internally Documenting SAS Program
Basic Debugging
Getting Help
Exercises
2. Reading, Creating and Formatting Data Sets
What does a SAS Data Step do?
Reading Data from External Files
Reading CSV, Excel and TEXT files
Temporary versus Permanent Status of Data Sets
Formatting and Labeling Variables
User-defined Formatting
Recoding and Transforming Variables in a DATA Step
Writing Out a File or Making a Simple Report
Exercises
3. Programming a DATA step
Writing Programs by subdividing tasks
Ordering How Tasks are Done
Index-able Lists of variables, aka arrays
Functions associated with Statistical Distributions
Generating Variables Using Random Number Generators
Remembering Variable Values across Observations
Processing multiple observations for a single observation
Case Study 1: Is the Two-Sample t-Test Robust to Violations of the Heterogeneous Variance assumption?
Efficiency considerations – how long does it take?
Case Study 2: Monte Carlo Integration to Estimate an Integral
Case Study 3: Simple Percentile-Based Bootstrap
Case Study 4: Randomization Test for the Equality of Two Populations
Exercises
4. Combining, extracting and reshaping data
Adding observations by SET-ing data sets
Adding variables by MERGE-ing data sets
Working with tables in PROC SQL
Converting wide to long formats
Converting long to wide formats
Case Study: Reshaping a World Bank data set
Building training and validation data sets
Exercises
Self-Study lab
5. Macro Programming
What Is a Macro and Why Would You Use It?
Motivation for Macros: Numerical Integration to Determine P(0<Z<1.645)
Processing Macros
Macro Variables, Parameters, and Functions
Conditional Execution, Looping, and Macros
Saving Macros
Functions and Routines for Macros
Case Study: Macro for constructing training and test data set for Model Comparison
Case Study: Processing Multiple Data Sets
Exercises
6. Customizing Output and Generating Data Visualizations
Using the Output Delivery System
Graphics in SAS
ODS Statistical Graphics
Modifying Graphics Using the ODS Graphics Editor
Graphing with Styles and Templates
Statistical Graphics—Entering the Land of SG Procedures
Case Study: Using the SG Procedures
Enhancing SG displays – options with SG procedure statements
Using Annotate Data Sets to enhance SG displays
Using Attribute Maps to enhance SG displays
Exercises
7. Processing Text
Cleaning and Processing Text Data
Starting with Character Functions
Processing Text
Case Study: Sentiment in State of the Union addresses
Case Study: Reading Text from a Web Page
Regular Expressions
Case Study (revisited) – Applying Regular Expressions
Exercises
8. Programming with Matrices and Vectors
Defining a Matrix and Subscripting
Using Diagonal Matrices and Stacking Matrices
Using Elementwise Operations, Repeating, and Multiplying Matrices
Importing a Data Set into SAS/IML and Exporting Matrices from SAS/IML to a Data Set
Case Study 1: Monte Carlo Integration to Estimate π
Case Study 2: Bisection Root Finder
Case Study 3: Randomization Test Using Matrices Imported from PROC PLAN
Case Study 4: SAS/IML Module to Implement Monte Carlo Integration to Estimate π
Storing and loading SAS/IML modules
SAS/IML and R
Exercises
References. Statistical Programming in SAS Second Edition provides a foundation for programming to implement statistical solutions using SAS, a system that has been used to solve data analytic problems for more than 40 years. The author includes motivating examples to inspire readers to generate programming solutions. Upper-level undergraduates, beginning graduate students, and professionals involved in generating programming solutions for data-analytic problems will benefit from this book. The ideal background for a reader is some background in regression modeling and introductory experience with computer programming.
The coverage of statistical programming in the second edition includes
Getting data into the SAS system, engineering new features, and formatting variables
Writing readable and well-documented code
Structuring, implementing, and debugging programs that are well documented
Creating solutions to novel problems
Combining data sources, extracting parts of data sets, and reshaping data sets as needed for other analyses
Generating general solutions using macros
Customizing output
Producing insight-inspiring data visualizations
Parsing, processing, and analyzing text
Programming solutions using matrices and connecting to R
Processing text
Programming with matrices
Connecting SAS with R
Covering topics that are part of both base and certification exams. </p>
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<p>Date Published:2020</p>
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</table>Technical aptitude for interviews : computer science and IT
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Sharma, Ela Kashyap
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<p> IT industry offers lucrative job opportunities not only for the IT graduates but also for all those non-IT background students who thrive to build their career in this field. This book, now in its second edition, apprises the reader with every minute detail of the IT concepts and serves as a self-help guide for the graduates and students appearing for their placement tests and interviews in the final year.
The book begins with the details of recruitment process and focuses on tackling difficult HR interview questions, resume building tips and provides sample resume which will equip the students for the interviews and hone their overall personality. The testimonials by the industry experts and academicians succinctly tell about the expectations of industry employers from the new recruits. The text in the middle chapters elaborates the programming concepts of C, C++ and Java as well as the concepts related to database, software engineering, operating systems, networking and DOT NET in great detail. The last chapter of the book presents a number of topics relating to general computer science aptitude.
NEW TO THE SECOND EDITION
•Numerous sections and examples have been included in chapters on OOP Concepts—Classes and Objects, Inheritance in C++, Polymorphism, Exception Handling and Templates in C++ and Operating System Concepts.
•Completely revamped text in the chapter on Database Concepts.
•Several MCQs from the latest interviews have now been incorporated into the respective chapters.
•Five sample test papers with solutions are provided for practice.
KEY FEATURES
•Includes questions gathered from the interviews conducted by companies such as Virtusa, TCS, IBM, DELL, HCL, Aon Hewitt, Convergys, CSC and Wipro.
•Serves as a complete guide containing basic programming concepts helpful for non-IT background students as well. </p>
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<p>Date Published:2017</p>
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</table>Nimaz ki sab say bari kitab
https://nu.insigniails.com/Library/Index?SearchType=titles&PassedInValue=Nimaz ki sab say bari kitab&LibraryID=All
Zia-ul-Qran Publications
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<a href='https://nu.insigniails.com/Library/Index?SearchType=titles&PassedInValue=Nimaz ki sab say bari kitab&LibraryID=All'>
<img src='https://nu.insigniails.com/Library/images/~imageCT89977.JPG' alt='Cover Image' width='80' height='110' border='0'>
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<p> </p>
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<p>Date Published:2019</p>
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</table>Quran ki rooshni mian jadeed riasati nizam
https://nu.insigniails.com/Library/Index?SearchType=titles&PassedInValue=Quran ki rooshni mian jadeed riasati nizam&LibraryID=All
Faheem, Raza
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<a href='https://nu.insigniails.com/Library/Index?SearchType=titles&PassedInValue=Quran ki rooshni mian jadeed riasati nizam&LibraryID=All'>
<img src='https://nu.insigniails.com/Library/images/~imageCT89976.JPG' alt='Cover Image' width='80' height='110' border='0'>
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<p> </p>
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<p>Date Published:2014</p>
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</table>The battle for Pakistan : the bitter US friendship and a tough neighbourhood
https://nu.insigniails.com/Library/Index?SearchType=titles&PassedInValue=The battle for Pakistan : the bitter US friendship and a tough neighbourhood&LibraryID=All
Nawaz, Shuja,
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<a href='https://nu.insigniails.com/Library/Index?SearchType=titles&PassedInValue=The battle for Pakistan : the bitter US friendship and a tough neighbourhood&LibraryID=All'>
<img src='https://nu.insigniails.com/Library/images/~imageCT89975.JPG' alt='Cover Image' width='80' height='110' border='0'>
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<p> The book covers the US-Pakistan relations, civil-military relations, and the nature of Pakistani politics. It sheds light onto the primarily concealed 'deep state' of Pakistan as well. In the concluding chapters, the author has presented an analysis on the future of Pakistan keeping in view the geopolitical, domestic, environmental, and security challenges confronting it. The
The author has defined a clear path for Pakistan in its relations with the U.S., India, Afghanistan, Iran, China, and other crucial actors on the global stage. Mr. Nawaz has also pointed out the fallacies in the military and civilian bureaucracy of Pakistan.
Overall, the book should be an interesting read to those who are interested in knowing more about the domestic and international politics of Pakistan. More importantly, it explains the social, historical, bureaucratic, economic, and security-related factors which have and continue to guide its policies. </p>
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<p>Date Published:2019</p>
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</table>Discrete structures and graph theory
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Patrai, K.
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<a href='https://nu.insigniails.com/Library/Index?SearchType=titles&PassedInValue=Discrete structures and graph theory&LibraryID=All'>
<img src='https://nu.insigniails.com/Library/images/~imageCT89974.JPG' alt='Cover Image' width='80' height='110' border='0'>
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<p> Includes index • Set Theory
• Relation
• Function
• Algebraic Structures
• Rings and Fields
• Posets, Hasse Diagrams and Lattices
• Boolean Algebra
• Propositional Calculus and Formal Logic
• Methods of Proof
• Graphs
• Euler and Hamiltonian Graphs
• Trees
• Planar Graphs
• Matrix Representation of Graphs
• Colouring of Graphs
• Finite State Machines
• Combinatorics
• Examination Papers. </p>
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<p>Date Published:2016</p>
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</table>Discrete mathematics
https://nu.insigniails.com/Library/Index?SearchType=titles&PassedInValue=Discrete mathematics&LibraryID=All
Zhang, Ping, 1957-
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<a href='https://nu.insigniails.com/Library/Index?SearchType=titles&PassedInValue=Discrete mathematics&LibraryID=All'>
<img src='https://nu.insigniails.com/Library/images/~imageCT89973.JPG' alt='Cover Image' width='80' height='110' border='0'>
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<p> Includes index What is discrete mathematics? --
Logic --
Sets --
Methods of proof --
Mathematical induction --
Relations and functions --
Algorithms and complexity --
Integers --
Introduction to counting --
Advanced counting methods --
Discrete probability --
Partially ordered sets and boolean algebras --
Introduction to graphs --
Trees --
Planar graphs and graph colorings --
Directed graphs. Chartrand and Zhangs Discrete Mathematics presents a clearly written, student-friendly introduction to discrete mathematics. The authors draw from their background as researchers and educators to offer lucid discussions and descriptions fundamental to the subject of discrete mathematics. Unique among discrete mathematics textbooks for its treatment of proof techniques and graph theory, topics discussed also include logic, relations and functions (especially equivalence relations and bijective functions), algorithms and analysis of algorithms, introduction to number theory, combinatorics (counting, the Pascal triangle, and the binomial theorem), discrete probability, partially ordered sets, lattices and Boolean algebras, cryptography, and finite-state machines.
This highly versatile text provides mathematical background used in a wide variety of disciplines, including mathematics and mathematics education, computer science, biology, chemistry, engineering, communications, and business.
Some of the major features and strengths of this textbook
Numerous, carefully explained examples and applications facilitate learning.
More than 1,600 exercises, ranging from elementary to challenging, are included with hints/answers to all odd-numbered exercises.
Descriptions of proof techniques are accessible and lively.
Students benefit from the historical discussions throughout the textbook. </p>
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<p>Date Published:2017</p>
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</table>The AVR microcontroller and embedded systems : using Assembly and C
https://nu.insigniails.com/Library/Index?SearchType=titles&PassedInValue=The AVR microcontroller and embedded systems : using Assembly and C&LibraryID=All
Mazidi, Muhammad Ali.
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<a href='https://nu.insigniails.com/Library/Index?SearchType=titles&PassedInValue=The AVR microcontroller and embedded systems : using Assembly and C&LibraryID=All'>
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<p> Includes index. The AVR Microcontroller and Embedded Systems: Using Assembly and C features a step-by-step approach in covering both Assembly and C language programming of the AVR family of Microcontrollers. It offers a systematic approach in programming and interfacing of the AVR with LCD, keyboard, ADC, DAC, Sensors, Serial Ports, Timers, DC and Stepper Motors, Opto-isolators, and RTC. Both Assembly and C languages are used in all the peripherals programming. In the first 6 chapters, Assembly language is used to cover the AVR architecture and starting with chapter 7, both Assembly and C languages are used to show the peripherals programming and interfacing. </p>
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<p>Date Published:2014</p>
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</table>Using OpenMP -- the next step : affinity, accelerators, tasking, and SIMD
https://nu.insigniails.com/Library/Index?SearchType=titles&PassedInValue=Using OpenMP -- the next step : affinity, accelerators, tasking, and SIMD&LibraryID=All
Pas, Ruud van der,
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<p> This book offers an up-to-date, practical tutorial on advanced features in the widely used OpenMP parallel programming model. Building on the previous volume, Using OpenMP: Portable Shared Memory Parallel Programming (MIT Press), this book goes beyond the fundamentals to focus on what has been changed and added to OpenMP since the 2.5 specifications. It emphasizes four major and advanced areas: thread affinity (keeping threads close to their data), accelerators (special hardware to speed up certain operations), tasking (to parallelize algorithms with a less regular execution flow), and SIMD (hardware assisted operations on vectors).
As in the earlier volume, the focus is on practical usage, with major new features primarily introduced by example. Examples are restricted to C and C++, but are straightforward enough to be understood by Fortran programmers. After a brief recap of OpenMP 2.5, the book reviews enhancements introduced since 2.5. It then discusses in detail tasking, a major functionality enhancement; Non-Uniform Memory Access (NUMA) architectures, supported by OpenMP; SIMD, or Single Instruction Multiple Data; heterogeneous systems, a new parallel programming model to offload computation to accelerators; and the expected further development of OpenMP. </p>
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<p>Date Published:2017</p>
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