The Text Was Designed To Familiarize Students With The Foundations And Principles Of Computer Science And To Strengthen The Students' Ability To Carry Out Formal And Rigorous Mathematical Arguments. In The New Fourth Edition, Author Peter Linz Has Offered A Straightforward, Uncomplicated Treatment Of Formal Languages And Automata And Avoids Excessive Mathematical Detail So That Students May Focus On And Understand The Underlying Principles. In An Effort To Further The Accessibility And Comprehension Of The Text, The Author Has Added New Illustrative Examples Throughout.

    With this practical book, you’ll learn how pointers provide the mechanism to dynamically manipulate memory, enhance support for data structures, and enable access to hardware. Author Richard Reese shows you how to use pointers with arrays, strings, structures, and functions, using memory models throughout the book.Difficult to master, pointers provide C with much flexibility and power—yet few resources are dedicated to this data type. This comprehensive book has the information you need, whether you’re a beginner or an experienced C or C++ programmer or developer.Get an introduction to pointers, including the declaration of different pointer typesLearn about dynamic memory allocation, de-allocation, and alternative memory management techniquesUse techniques for passing or returning data to and from functionsUnderstand the fundamental aspects of arrays as they relate to pointersExplore the basics of strings and how pointers are used to support themExamine why pointers can be the source of security problems, such as buffer overflowLearn several pointer techniques, such as the use of opaque pointers, bounded pointers and, the restrict keyword

    Daniel Liang teaches concepts of problem-solving and object-oriented programming using a fundamentals-first approach. Beginning programmers learn critical problem-solving techniques then move on to grasp the key concepts of object-oriented and GUI programming using Java 5. Students start with the essential problem-solving and programming concepts (control statements, methods, and arrays), are then introduced to object-oriented programming, graphical user interface (GUI), and applets, and finally to exception handling, I/O, data structures, and other advanced subjects. Liang uses small, simple, and stimulating examples to demonstrate concepts and techniques while longer examples are presented in case studies with overall discussions and thorough line-by-line explanations. Students can now write short, interesting, graphical game programs starting from Chapter 2! reinforcing key concepts with objectives lists, introduction and chapter overviews, easy to follow examples, chapter summaries, review questions, programming exercises, interactive self-test. Students receive solutions to even-numbered programming exercises, source code for the examples in the book, online self assessment (w/over 1000 multiple-choice questions) and ONLINE homework through GRADIANCE, the industries most advanced online homework application. Instructors are offered the most extensive library of support materials available including interactive and animated slides, TestGen (w/over 2000 multiple-choice questions), solutions to all programming exercises, sample exams and supplemental exercises. Available in two versions, the Fundamentals First edition (chapters 1-19) and the Comprehensive version (chapters 1-36).

    To grow professionally, you also need soft skills and effective learning techniques. Honing those skills is what this book is all about. Authors Dave Hoover and Adewale Oshineye have cataloged dozens of behavior patterns to help you perfect essential aspects of your craft. Compiled from years of research, many interviews, and feedback from O'Reilly's online forum, these patterns address difficult situations that programmers, administrators, and DBAs face every day. And it's not just about financial success. Apprenticeship Patterns also approaches software development as a means to personal fulfillment. Discover how this book can help you make the best of both your life and your career. Solutions to some common obstacles that this book explores in-depth include:Burned out at work? "Nurture Your Passion" by finding a pet project to rediscover the joy of problem solving.Feeling overwhelmed by new information? Re-explore familiar territory by building something you've built before, then use "Retreat into Competence" to move forward again.Stuck in your learning? Seek a team of experienced and talented developers with whom you can "Be the Worst" for a while. "Brilliant stuff! Reading this book was like being in a time machine that pulled me back to those key learning moments in my career as a professional software developer and, instead of having to learn best practices the hard way, I had a guru sitting on my shoulder guiding me every step towards master craftsmanship. I'll certainly be recommending this book to clients. I wish I had this book 14 years ago!" -Russ Miles, CEO, OpenCredo

    The book focuses on techniques of lasting value and explains them precisely in terms of a simple abstract machine. The book presents all major programming paradigms in a uniform framework that shows their deep relationships and how and where to use them together.After an introduction to programming concepts, the book presents both well-known and lesser-known computation models ("programming paradigms"). Each model has its own set of techniques and each is included on the basis of its usefulness in practice. The general models include declarative programming, declarative concurrency, message-passing concurrency, explicit state, object-oriented programming, shared-state concurrency, and relational programming. Specialized models include graphical user interface programming, distributed programming, and constraint programming. Each model is based on its kernel language—a simple core language that consists of a small number of programmer- significant elements. The kernel languages are introduced progressively, adding concepts one by one, thus showing the deep relationships between different models. The kernel languages are defined precisely in terms of a simple abstract machine. Because a wide variety of languages and programming paradigms can be modeled by a small set of closely related kernel languages, this approach allows programmer and student to grasp the underlying unity of programming. The book has many program fragments and exercises, all of which can be run on the Mozart Programming System, an Open Source software package that features an interactive incremental development environment.

    It takes a fairly traditional approach, emphasizing problem solving, design, and programming as the core skills of computer science. However, these ideas are illustrated using a non-traditional language, namely Python. Although I use Python as the language, teaching Python is not the main point of this book. Rather, Python is used to illustrate fundamental principles of design and programming that apply in any language or computing environment. In some places, I have purposely avoided certain Python features and idioms that are not generally found in other languages. There are already many good books about Python on the market; this book is intended as an introduction to computing. Features include the following: *Extensive use of computer graphics. *Interesting examples. *Readable prose. *Flexible spiral coverage. *Just-in-time object coverage. *Extensive end-of-chapter problems.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    Weinberg adds new insights and highlights the similarities and differences between now and then. Using a conversational style that invites the reader to join him, Weinberg reunites with some of his most insightful writings on the human side of software engineering.Topics include egoless programming, intelligence, psychological measurement, personality factors, motivation, training, social problems on large projects, problem-solving ability, programming language design, team formation, the programming environment, and much more.Dorset House Publishing is proud to make this important text available to new generations of programmers -- and to encourage readers of the first edition to return to its valuable lessons.

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    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."

    With the many upgraded features, C# is more expressive than ever. However, an in depth understanding is required to get the most out of the language.C# in Depth, Second Edition is a thoroughly revised, up-to-date book that covers the new features of C# 4 as well as Code Contracts. In it, you'll see the subtleties of C# programming in action, learning how to work with high-value features that you'll be glad to have in your toolkit. The book helps readers avoid hidden pitfalls of C# programming by understanding "behind the scenes" issues.Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

    Algorithms in a Nutshell describes a large number of existing algorithms for solving a variety of problems, and helps you select and implement the 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 solutionQuickly locate algorithms that relate to the problems you want to solve, and determine why a particular algorithm is the right one to useGet algorithmic solutions in C, C++, Java, and Ruby with implementation tipsLearn the expected performance of an algorithm, and the conditions it needs to perform at its bestDiscover the impact that similar design decisions have on different algorithmsLearn advanced data structures to improve the efficiency of algorithmsWith Algorithms in a Nutshell, you'll learn how to improve the performance of key algorithms essential for the success of your software applications.

    Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques.The book deals with some of the most important and challenging areas of programming and computer science, but in a highly pedagogic and readable manner. The book covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others himself.

    In this book, you'll learn how to become an entrepreneur, driving your career in the direction of your choosing. You'll learn how to build your software development career step by step, following the same path that you would follow if you were building, marketing, and selling a product. After all, your skills themselves are a product. The choices you make about which technologies to focus on and which business domains to master have at least as much impact on your success as your technical knowledge itself--don't let those choices be accidental. We'll walk through all aspects of the decision-making process, so you can ensure that you're investing your time and energy in the right areas. You'll develop a structured plan for keeping your mind engaged and your skills fresh. You'll learn how to assess your skills in terms of where they fit on the value chain, driving you away from commodity skills and toward those that are in high demand. Through a mix of high-level, thought-provoking essays and tactical "Act on It" sections, you will come away with concrete plans you can put into action immediately. You'll also get a chance to read the perspectives of several highly successful members of our industry from a variety of career paths. As with any product or service, if nobody knows what you're selling, nobody will buy. We'll walk through the often-neglected world of marketing, and you'll create a plan to market yourself both inside your company and to the industry in general. Above all, you'll see how you can set the direction of your career, leading to a more fulfilling and remarkable professional life.

     Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.