Computer Graphics with OpenGL


Donald Hearn - 2003
    The text converts all programming code into the C++ language.

Elementary Statistics: A Step by Step Approach


Allan G. Bluman - 1992
    The book is non-theoretical, explaining concepts intuitively and teaching problem solving through worked examples and step-by-step instructions. This edition places more emphasis on conceptual understanding and understanding results. This edition also features increased emphasis on Excel, MINITAB, and the TI-83 Plus and TI 84-Plus graphing calculators, computing technologies commonly used in such courses.

Black Hat Python: Python Programming for Hackers and Pentesters


Justin Seitz - 2014
    But just how does the magic happen?In Black Hat Python, the latest from Justin Seitz (author of the best-selling Gray Hat Python), you'll explore the darker side of Python's capabilities writing network sniffers, manipulating packets, infecting virtual machines, creating stealthy trojans, and more. You'll learn how to:Create a trojan command-and-control using GitHubDetect sandboxing and automate common malware tasks, like keylogging and screenshottingEscalate Windows privileges with creative process controlUse offensive memory forensics tricks to retrieve password hashes and inject shellcode into a virtual machineExtend the popular Burp Suite web-hacking toolAbuse Windows COM automation to perform a man-in-the-browser attackExfiltrate data from a network most sneakilyInsider techniques and creative challenges throughout show you how to extend the hacks and how to write your own exploits.When it comes to offensive security, your ability to create powerful tools on the fly is indispensable. Learn how in Black Hat Python."

Cryptography Engineering: Design Principles and Practical Applications


Niels Ferguson - 2010
    Cryptography is vital to keeping information safe, in an era when the formula to do so becomes more and more challenging. Written by a team of world-renowned cryptography experts, this essential guide is the definitive introduction to all major areas of cryptography: message security, key negotiation, and key management. You'll learn how to think like a cryptographer. You'll discover techniques for building cryptography into products from the start and you'll examine the many technical changes in the field.After a basic overview of cryptography and what it means today, this indispensable resource covers such topics as block ciphers, block modes, hash functions, encryption modes, message authentication codes, implementation issues, negotiation protocols, and more. Helpful examples and hands-on exercises enhance your understanding of the multi-faceted field of cryptography.An author team of internationally recognized cryptography experts updates you on vital topics in the field of cryptography Shows you how to build cryptography into products from the start Examines updates and changes to cryptography Includes coverage on key servers, message security, authentication codes, new standards, block ciphers, message authentication codes, and more Cryptography Engineering gets you up to speed in the ever-evolving field of cryptography.

Computer Age Statistical Inference: Algorithms, Evidence, and Data Science


Bradley Efron - 2016
    'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

Physics for Scientists and Engineers, Volume 2


Raymond A. Serway - 1982
    Raymond Serway, Robert Beichner, and contributing author John W. Jewett present a strong problem-solving approach that is further enhanced through increased realism in worked examples. Problem-solving strategies and hints allow students to develop a systematic approach to completing homework problems. The outstanding ancillary package includes full multimedia support, online homework, and a content-rich Web site that provides extensive support for instructors and students. The CAPA (Computer-assisted Personalized Approach), WebAssign, and University of Texas homework delivery systems give instructors flexibility in assigning online homework.

Working with UNIX Processes


Jesse Storimer - 2011
    Want to impress your coworkers and write the fastest, most efficient, stable code you ever have? Don't reinvent the wheel. Reuse decades of research into battle-tested, highly optimized, and proven techniques available on any Unix system.This book will teach you what you need to know so that you can write your own servers, debug your entire stack when things go awry, and understand how things are working under the hood.http://www.jstorimer.com/products/wor...

Programming in Python 3: A Complete Introduction to the Python Language


Mark Summerfield - 2008
    It brings together all the knowledge needed to write any program, use any standard or third-party Python 3 library, and create new library modules of your own.

Make Your Own Neural Network: An In-depth Visual Introduction For Beginners


Michael Taylor - 2017
    A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow.

Principles of Information Security


Michael E. Whitman - 2002
    Principles of Information Security, Third Edition builds on internationally recognized standards and bodies of knowledge to provide the knowledge and skills that information systems students need for their future roles as business decision-makers. Coverage includes key knowledge areas of the CISSP (Certified Information Systems Security Professional), as well as risk management, cryptography, physical security, and more. The third edition has retained the real-world examples and scenarios that made previous editions so successful, but has updated the content to reflect technology's latest capabilities and trends. With this emphasis on currency and comprehensive coverage, readers can feel confident that they are using a standards-based, content-driven resource to prepare them for their work in the field.

Visual Complex Analysis


Tristan Needham - 1997
    Aimed at undergraduate students in mathematics, physics, and engineering, the book's intuitive explanations, lack ofadvanced prerequisites, and consciously user-friendly prose style will help students to master the subject more readily than was previously possible. The key to this is the book's use of new geometric arguments in place of the standard calculational ones. These geometric arguments are communicatedwith the aid of hundreds of diagrams of a standard seldom encountered in mathematical works. A new approach to a classical topic, this work will be of interest to students in mathematics, physics, and engineering, as well as to professionals in these fields.

R Graphics Cookbook: Practical Recipes for Visualizing Data


Winston Chang - 2012
    Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you're ready to get started.Use R's default graphics for quick exploration of dataCreate a variety of bar graphs, line graphs, and scatter plotsSummarize data distributions with histograms, density curves, box plots, and other examplesProvide annotations to help viewers interpret dataControl the overall appearance of graphicsRender data groups alongside each other for easy comparisonUse colors in plotsCreate network graphs, heat maps, and 3D scatter plotsStructure data for graphing

Programming Languages: Design and Implementation


Terrence W. Pratt - 1995
    The emphasis throughout is on fundamental concepts--readers learn important ideas, not minor language differences--but several languages are highlighted in sufficient detail to enable readers to write programs that demonstrate the relationship between a source program and its execution behavior--e.g., C, C++, JAVA, ML, LISP, Prolog, Smalltalk, Postscript, HTML, PERL, FORTRAN, Ada, COBOL, BASIC SNOBOL4, PL/I, Pascal. Begins with a background review of programming languages and the underlying hardware that will execute the given program; then covers the underlying grammatical model for programming languages and their compilers (elementary data types, data structures and encapsulation, inheritance, statements, procedure invocation, storage management, distributed processing, and network programming). Includes an advanced chapter on language semantics--program verification, denotational semantics, and the lambda calculus. For computer engineers and others interested in programming language designs.

How to Count (Programming for Mere Mortals, #1)


Steven Frank - 2011
    unsigned numbers- Floating point and fixed point arithmeticThis short, easily understood book will quickly get you thinking like a programmer.

Decision Trees and Random Forests: A Visual Introduction For Beginners: A Simple Guide to Machine Learning with Decision Trees


Chris Smith - 2017
     They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk. Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact. This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own machine learning algorithms in Python, this book is for you.