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Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. Summary Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. You’ll work through a series of exercises based in computer science fundamentals that are designed to improve your software development abilities, improve your understanding of artificial intelligence, and even prepare you to ace an interview. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Whatever software development problem you’re facing, odds are someone has already uncovered a solution. This book collects the most useful solutions devised, guiding you through a variety of challenges and tried-and-true problem-solving techniques. The principles and algorithms presented here are guaranteed to save you countless hours in project after project. About the book Classic Computer Science Problems in Java is a master class in computer programming designed around 55 exercises that have been used in computer science classrooms for years. You’ll work through hands-on examples as you explore core algorithms, constraint problems, AI applications, and much more. What's inside Recursion, memoization, and bit manipulation Search, graph, and genetic algorithms Constraint-satisfaction problems K-means clustering, neural networks, and adversarial search About the reader For intermediate Java programmers. About the author David Kopec is an assistant professor of Computer Science and Innovation at Champlain College in Burlington, Vermont. Table of Contents 1 Small problems 2 Search problems 3 Constraint-satisfaction problems 4 Graph problems 5 Genetic algorithms 6 K-means clustering 7 Fairly simple neural networks 8 Adversarial search 9 Miscellaneous problems 10 Interview with Brian Goetz
"Whether you're a novice or a seasoned professional, there's an Aha! moment in this book for everyone." - James Watson, Adaptive ”Highly recommended to everyone interested in deepening their understanding of Python and practical computer science.” —Daniel Kenney-Jung, MD, University of Minnesota Key Features • Master formal techniques taught in college computer science classes • Connect computer science theory to real-world applications, data, and performance • Prepare for programmer interviews • Recognize the core ideas behind most “new” challenges • Covers Python 3.7 Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Programming problems that seem new or unique are usually rooted in well-known engineering principles. Classic Computer Science Problems in Python guides you through time-tested scenarios, exercises, and algorithms that will prepare you for the “new” problems you’ll face when you start your next project. In this amazing book, you'll tackle dozens of coding challenges, ranging from simple tasks like binary search algorithms to clustering data using k-means. As you work through examples for web development, machine learning, and more, you'll remember important things you've forgotten and discover classic solutions that will save you hours of time. What You Will Learn • Search algorithms • Common techniques for graphs • Neural networks • Genetic algorithms • Adversarial search • Uses type hints throughout This Book Is Written For For intermediate Python programmers. About The Author David Kopec is an assistant professor of Computer Science and Innovation at Champlain College in Burlington, Vermont. He is the author of Dart for Absolute Beginners (Apress, 2014), Classic Computer Science Problems in Swift (Manning, 2018), and Classic Computer Science Problems in Java (Manning, 2020) Table of Contents 1. Small problems 2. Search problems 3. Constraint-satisfaction problems 4. Graph problems 5. Genetic algorithms 6. K-means clustering 7. Fairly simple neural networks 8. Adversarial search 9. Miscellaneous problems
Summary Classic Computer Science Problems in Swift invites readers to invest their energy in some foundational techniques that have been proven to stand the test of time. Along the way they'll learn intermediate and advanced features of the Swift programming language, a worthwhile skill in its own right. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Don't just learn another language. Become a better programmer instead. Today's awesome iOS apps stand on the shoulders of classic algorithms, coding techniques, and engineering principles. Master these core skills in Swift, and you'll be ready for AI, data-centric programming, machine learning, and the other development challenges that will define the next decade. About the Book Classic Computer Science Problems in Swift deepens your Swift language skills by exploring foundational coding techniques and algorithms. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems. You'll appreciate author David Kopec's amazing ability to connect the core disciplines of computer science to the real-world concerns of apps, data, performance, and even nailing your next job interview! What's Inside Breadth-first, depth-first, and A* search algorithms Constraint-satisfaction problems Solving problems with graph algorithms Neural networks, genetic algorithms, and more All examples written in Swift 4.1 About the Reader For readers comfortable with the basics of Swift. About the Author David Kopec is an assistant professor of computer science and innovation at Champlain College in Burlington, Vermont. He is an experienced iOS developer and the author of Dart for Absolute Beginners. Table of Contents Small problems Search problems Constraint-satisfaction problems Graph problems Genetic algorithms K-means clustering Fairly simple neural networks Miscellaneous problems
Dart for Absolute Beginners enables individuals with no background in programming to create their own web apps while learning the fundamentals of software development in a cutting edge language. Easily digested chapters, while comprehensive enough to explore the whole domain, are aimed at both hobbyists and professionals alike. The reader will not only gain an insight into Dart, but also the technologies behind the web. A firm foundation is laid for further programming studies. Dart is a new, innovative language developed by Google which is poised to take the web by storm. For client side web app development, Dart has many advantages over JavaScript. These include but are not limited to: improved speed, enforcement of programmatic structure, and improved facilities for software reuse. Best of all, Dart is automatically converted to JavaScript so that it works with all web browsers. Dart is a fresh start, without the baggage of the last two decades of the web. Why start learning to program with yesterday’s technology? Teaches you the fundamentals of programming and the technologies behind the web. Utilizes the cutting edge, easy to learn, structured Dart programming language so that your first steps are pointed towards the future of web development. No prior knowledge is required to begin developing your own web apps.
In The Art and Science of Java, Stanford professor and well-known leader in Computer Science Education Eric Roberts emphasizes the reader-friendly exposition that led to the success of The Art and Science of C. By following the recommendations of the Association of Computing Machinery's Java Task Force, this first edition text adopts a modern objects-first approach that introduces readers to useful hierarchies from the very beginning. Introduction; Programming by Example; Expressions; Statement Forms; Methods; Objects and Classes; Objects and Memory; Strings and Characters; Object-Oriented Graphics; Event-Driven Programs; Arrays and ArrayLists; Searching and Sorting; Collection Classes; Looking Ahead. A modern objects-first approach to the Java programming language that introduces readers to useful class hierarchies from the very beginning.
Currently used at many colleges, universities, and high schools, this hands-on introduction to computer science is ideal for people with little or no programming experience. The goal of this concise book is not just to teach you Java, but to help you think like a computer scientist. You’ll learn how to program—a useful skill by itself—but you’ll also discover how to use programming as a means to an end. Authors Allen Downey and Chris Mayfield start with the most basic concepts and gradually move into topics that are more complex, such as recursion and object-oriented programming. Each brief chapter covers the material for one week of a college course and includes exercises to help you practice what you’ve learned. Learn one concept at a time: tackle complex topics in a series of small steps with examples Understand how to formulate problems, think creatively about solutions, and write programs clearly and accurately Determine which development techniques work best for you, and practice the important skill of debugging Learn relationships among input and output, decisions and loops, classes and methods, strings and arrays Work on exercises involving word games, graphics, puzzles, and playing cards
Functional and flexible, this guide takes an objects-first approach to Java programming and problem using games and puzzles. Updated to cover Java version 1.5 features, such as generic types, enumerated types, and the Scanner class. Offers independent introductions to both a command-line interface and a graphical user interface (GUI). Features coverage of Unified Modeling Language (UML), the industry-standard, object-oriented design tool. Illustrates key aspects of Java with a collection of game and puzzle examples. Instructor and Student resources available online. For introductory computer programming students or professionals interested in learning Java.
Explore a wide variety of popular interview questions and learn various techniques for breaking down tricky bits of code and algorithms into manageable chunks Key FeaturesDiscover over 200 coding interview problems and their solutions to help you secure a job as a Java developerWork on overcoming coding challenges faced in a wide array of topics such as time complexity, OOP, and recursionGet to grips with the nuances of writing good code with the help of step-by-step coding solutionsBook Description Java is one of the most sought-after programming languages in the job market, but cracking the coding interview in this challenging economy might not be easy. This comprehensive guide will help you to tackle various challenges faced in a coding job interview and avoid common interview mistakes, and will ultimately guide you toward landing your job as a Java developer. This book contains two crucial elements of coding interviews - a brief section that will take you through non-technical interview questions, while the more comprehensive part covers over 200 coding interview problems along with their hands-on solutions. This book will help you to develop skills in data structures and algorithms, which technical interviewers look for in a candidate, by solving various problems based on these topics covering a wide range of concepts such as arrays, strings, maps, linked lists, sorting, and searching. You'll find out how to approach a coding interview problem in a structured way that produces faster results. Toward the final chapters, you'll learn to solve tricky questions about concurrency, functional programming, and system scalability. By the end of this book, you'll have learned how to solve Java coding problems commonly used in interviews, and will have developed the confidence to secure your Java-centric dream job. What you will learnSolve the most popular Java coding problems efficientlyTackle challenging algorithms that will help you develop robust and fast logicPractice answering commonly asked non-technical interview questions that can make the difference between a pass and a failGet an overall picture of prospective employers' expectations from a Java developerSolve various concurrent programming, functional programming, and unit testing problemsWho this book is for This book is for students, programmers, and employees who want to be invited to and pass interviews given by top companies. The book assumes high school mathematics and basic programming knowledge.
Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide
Named a Notable Book in the 21st Annual Best of Computing list by the ACM! Robert Sedgewick and Kevin Wayne’s Computer Science: An Interdisciplinary Approach is the ideal modern introduction to computer science with Java programming for both students and professionals. Taking a broad, applications-based approach, Sedgewick and Wayne teach through important examples from science, mathematics, engineering, finance, and commercial computing. The book demystifies computation, explains its intellectual underpinnings, and covers the essential elements of programming and computational problem solving in today’s environments. The authors begin by introducing basic programming elements such as variables, conditionals, loops, arrays, and I/O. Next, they turn to functions, introducing key modular programming concepts, including components and reuse. They present a modern introduction to object-oriented programming, covering current programming paradigms and approaches to data abstraction. Building on this foundation, Sedgewick and Wayne widen their focus to the broader discipline of computer science. They introduce classical sorting and searching algorithms, fundamental data structures and their application, and scientific techniques for assessing an implementation’s performance. Using abstract models, readers learn to answer basic questions about computation, gaining insight for practical application. Finally, the authors show how machine architecture links the theory of computing to real computers, and to the field’s history and evolution. For each concept, the authors present all the information readers need to build confidence, together with examples that solve intriguing problems. Each chapter contains question-and-answer sections, self-study drills, and challenging problems that demand creative solutions. Companion web site (introcs.cs.princeton.edu/java) contains Extensive supplementary information, including suggested approaches to programming assignments, checklists, and FAQs Graphics and sound libraries Links to program code and test data Solutions to selected exercises Chapter summaries Detailed instructions for installing a Java programming environment Detailed problem sets and projects Companion 20-part series of video lectures is available at informit.com/title/9780134493831