The Ultimate C: Concepts, Programs and Interview Questions

The Ultimate C: Concepts, Programs and Interview Questions

The main objective of writing this book is that every student should be able to acquire necessary skills required to become a programmer. The logic of each and every problem is explained in a simple manner which helps the student to write better programs. This book discusses all concepts of C starting from fundamentals to advanced topics in a lucid manner. It covers hundreds of solved programs which are useful to the students for their examinations. It also covers the interview questions which help the students come up with flying colors in their career.

Salient Features:

  • All the concepts are discussed in a lucid, easy to understand manner.
  • A reader without any basic knowledge in computer s can comfortably follow this book.
  • Helps to build logic in the students which becomes stepping stone for programming.
  • Interview questions collected from the actual interviews of various Software companies will help the students to be successful in their campus interviews.
  • Hundreds of solved programs help the students of Indian Universities do well in their examinations like B.C.A, B.Sc, M.Sc, M.C.A, B.E, B.Tech, M.Tech, etc.
  • Works like a handy reference to the Software professionals in their programming.
  • Starting at basic level, this book covers advanced topics like Pointers, Data structures, Searching and sorting techniques and Graphics.

Table of Contents:

    1. Fundamental Concepts in C
    2. Data types and operators
    3. Control statements in C
    4. Arrays
    5. Functions
    6. Characters and strings
    7. Storage Classes
    8. Pointers
    9. Structures and Unions
    10. File Concepts
    11. Command Line Arguments
    12. Macros and Enumerations
    13. Data Structures in C
    14. Searching, Sorting and Merging
    15. Graphics and Animation
    Apendix – I: List of Programs
    Apendix – I :List of Interview Questions

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Deep Learning Cookbook

Deep Learning Cookbook
Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve deep-learning problems for classifying and generating text, images, and music. Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you’re stuck. Examples are written in Python with code available on GitHub as a set of Python notebooks. You’ll learn how to: Create applications that will serve real users Use word embeddings to calculate text similarity Build a movie recommender system based on Wikipedia links Learn how AIs see the world by visualizing their internal state Build a model to suggest emojis for pieces of text Reuse pretrained networks to build an inverse image search service Compare how GANs, autoencoders and LSTMs generate icons Detect music styles and index song collections
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Machine Learning: a Concise Introduction (Wiley Series in Probability and Statistics)

Machine Learning: a Concise Introduction (Wiley Series in Probability and Statistics)
AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL TECHNIQUES, METHODS, AND APPLICATIONS Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author–an expert in the field–presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. The design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding these principles leads to more flexible and successful applications. Machine Learning: a Concise Introduction also includes methods for optimization, risk estimation, and model selection– essential elements of most applied projects. This important resource: Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods Presents R source code which shows how to apply and interpret many of the techniques covered Includes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions Contains useful information for effectively communicating with clients A volume in the popular Wiley Series in Probability and Statistics, Machine Learning a Concise Introduction offers the practical information needed for an understanding of the methods and application of machine learning. STEVEN W. KNOX holds a Ph.D. in Mathematics from the University of Illinois and an M.S. in Statistics from Carnegie Mellon University. He has over twenty years’ experience in using Machine Learning, Statistics, and Mathematics to solve real-world problems. He currently serves as Technical Director of Mathematics Research and Senior Advocate for Data Science at the National Security Agency.
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Introduction to Security Reduction

Introduction to Security Reduction

This monograph illustrates important notions in security reductions and essential techniques in security reductions for group-based cryptosystems. Using digital signatures and encryption as examples, the authors explain how to program correct security reductions for those cryptographic primitives. Various schemes are selected and re-proven in this book to demonstrate and exemplify correct security reductions.

This book is suitable for researchers and graduate students engaged with public-key cryptography.

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Matplotlib for Python Developers: Effective techniques for data visualization with Python, 2nd Edition

Matplotlib for Python Developers: Effective techniques for data visualization with Python, 2nd Edition
Leverage the power of Matplotlib to visualize and understand your data more effectively Key Features Perform effective data visualization with Matplotlib and get actionable insights from your data Design attractive graphs, charts, and 2D plots, and deploy them to the web Get the most out of Matplotlib in this practical guide with updated code and examples Book DescriptionPython is a general-purpose programming language increasingly being used for data analysis and visualization. Matplotlib is a popular data visualization package in Python used to design effective plots and graphs. This is a practical, hands-on resource to help you visualize data with Python using the Matplotlib library. Matplotlib for Python Developers, Second Edition shows you how to create attractive graphs, charts, and plots using Matplotlib. You will also get a quick introduction to third-party packages, Seaborn, Pandas, Basemap, and Geopandas, and learn how to use them with Matplotlib. After that, you’ll embed and customize your plots in third-party tools such as GTK+3, Qt 5, and wxWidgets. You’ll also be able to tweak the look and feel of your visualization with the help of practical examples provided in this book. Further on, you’ll explore Matplotlib 2.1.x on the web, from a cloud-based platform using third-party packages such as Django. Finally, you will integrate interactive, real-time visualization techniques into your current workflow with the help of practical real-world examples. By the end of this book, you’ll be thoroughly comfortable with using the popular Python data visualization library Matplotlib 2.1.x and leveraging its power to build attractive, insightful, and powerful visualizations. What you will learn Create 2D and 3D static plots such as bar charts, heat maps, and scatter plots Get acquainted with GTK+3, Qt5, and wxWidgets to understand the UI backend of Matplotlib Develop advanced static plots with third-party packages such as Pandas, GeoPandas, and Seaborn Create interactive plots with real-time updates Develop web-based, Matplotlib-powered graph visualizations with third-party packages such as Django Write data visualization code that is readily expandable on the cloud platform Who this book is forThis book is essentially for anyone who wants to create intuitive data visualizations using the Matplotlib library. If you’re a data scientist or analyst and wish to create attractive visualizations using Python, you’ll find this book useful. Some knowledge of Python programming is all you need to get started.
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Your Happiness was Hacked

Your Happiness was Hacked

We’ve become a tribe of tech addicts, and it’s not entirely our fault.
Taking advantage of vulnerabilities in the human brain function, tech companies entice us to overdose on technology interaction. This damages our lives, work, families and friendships. Swipe-driven apps train us to evaluate people like products, diminishing our relationships. At work, we email on an average of seventy-seven times a day, ruining our concentration. At home, light from our screens contributes to epidemic sleep deprivation.
But we can reclaim our lives without dismissing technology. The authors explain how to avoid getting hooked on tech and how to define and control the roles that it plays and could play in our lives. This profound and timely book turns personal observation into a handy guide to adapting to our new reality of omnipresent technology.

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Learning Robotics using Python: Design, simulate, program, and prototype an autonomous mobile robot using ROS, OpenCV, PCL, and Python, 2nd Edition

Learning Robotics using Python: Design, simulate, program, and prototype an autonomous mobile robot using ROS, OpenCV, PCL, and Python, 2nd Edition
Design, simulate, and program interactive robots Key Features Design, simulate, build, and program an interactive autonomous mobile robot Leverage the power of ROS, Gazebo, and Python to enhance your robotic skills A hands-on guide to creating an autonomous mobile robot with the help of ROS and Python Book DescriptionRobot Operating System (ROS) is one of the most popular robotics software frameworks in research and industry. It has various features for implementing different capabilities in a robot without implementing them from scratch. This book starts by showing you the fundamentals of ROS so you understand the basics of differential robots. Then, you’ll learn about robot modeling and how to design and simulate it using ROS. Moving on, we’ll design robot hardware and interfacing actuators. Then, you’ll learn to configure and program depth sensors and LIDARs using ROS. Finally, you’ll create a GUI for your robot using the Qt framework. By the end of this tutorial, you’ll have a clear idea of how to integrate and assemble everything into a robot and how to bundle the software package. What you will learn Design a differential robot from scratch Model a differential robot using ROS and URDF Simulate a differential robot using ROS and Gazebo Design robot hardware electronics Interface robot actuators with embedded boards Explore the interfacing of different 3D depth cameras in ROS Implement autonomous navigation in ChefBot Create a GUI for robot control Who this book is forThis book is for those who are conducting research in mobile robotics and autonomous navigation. As well as the robotics research domain, this book is also for the robot hobbyist community. You’re expected to have a basic understanding of Linux commands and Python.
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