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  • Math for Programmers
    Math for Programmers

    To score a job in data science, machine learning, computer graphics, and cryptography, you need to bring strong math skills to the party.Math for Programmers teaches the math you need for these hot careers, concentrating on what you need to know as a developer. Filled with lots of helpful graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest programming fields. Key Features · 2D and 3D vector math · Matrices and linear transformations · Core concepts from linear algebra · Calculus with one or more variables · Algorithms for regression, classification, and clustering · Interesting real-world examples Written for programmers with solid algebra skills (even if they need some dusting off).No formal coursework in linear algebra or calculus is required. About the technology Most businesses realize they need to apply data science and effective machine learning to gain and maintain a competitive edge.To build these applications, they need developers comfortable writing code and using tools steeped in statistics, linear algebra, and calculus.Math also plays an integral role in other modern applications like game development, computer graphics and animation, image and signal processing, pricing engines, and stock market analysis. Paul Orland is CEO of Tachyus, a Silicon Valley startup building predictive analytics software to optimize energy production in the oil and gas industry.As founding CTO, he led the engineering team to productize hybrid machine learning and physics models, distributed optimization algorithms, and custom web-based data visualizations.He has a B.S. in mathematics from Yale University and a M.S. in physics from the University of Washington.

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  • Python for Programmers
    Python for Programmers

    The professional programmer's Deitel® guide to Python® with introductory artificial intelligence case studies Written for programmers with a background in another high-level language, Python for Programmers uses hands-on instruction to teach today's most compelling, leading-edge computing technologies and programming in Python--one of the world's most popular and fastest-growing languages.Please read the Table of Contents diagram inside the front cover and the Preface for more details. In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you'll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms.After covering Python Chapters 1-5 and a few key parts of Chapters 6-7, you'll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11-16, which are loaded with cool, powerful, contemporary examples.These include natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® WatsonTM, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop®, SparkTM and NoSQL databases, the Internet of Things and more.You'll also work directly or indirectly with cloud-based services, including Twitter, Google TranslateTM, IBM Watson, Microsoft® Azure®, OpenMapQuest, PubNub and more. Features 500+ hands-on, real-world, live-code examples from snippets to case studiesIPython + code in Jupyter® NotebooksLibrary-focused: Uses Python Standard Library and data science libraries to accomplish significant tasks with minimal codeRich Python coverage: Control statements, functions, strings, files, JSON serialization, CSV, exceptionsProcedural, functional-style and object-oriented programmingCollections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFramesStatic, dynamic and interactive visualizationsData experiences with real-world datasets and data sourcesIntro to Data Science sections: AI, basic stats, simulation, animation, random variables, data wrangling, regressionAI, big data and cloud data science case studies: NLP, data mining Twitter®, IBM® WatsonTM, machine learning, deep learning, computer vision, Hadoop®, SparkTM, NoSQL, IoTOpen-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn®, Keras and more Register your product to gain access to updated chapters and material, as well as downloads, future updates, and/or corrections as they become available.See inside book for more information.

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  • Geometry for Programmers
    Geometry for Programmers

    Master the geometry behind CAD, game engines, GIS, and more!Geometry for Programmers is a hands-on book teaching you the maths behind the tools and libraries to create simulations, 3D prints, and other models in the physical world. Ideal for developers writing code using CAD libraries, game engines, or rendering tools, the textbook guides you through the math behind graphics and modelling tools using relevant examples and clear explanations that don't require advanced mathematical knowledge.You will learn how mastering manual geometry can help you avoid code layering and repetition and even how to drive down cloud hosting costs by creating more efficient application runtimes. Key features include: Speak the language of applied geometryCompose geometric transformations economicallyCraft custom splines for efficient curves and surface generationPick and implement the right geometric transformationsConfidently use important algorithms that operate on triangle meshes, distance functions, and voxels Filled with charts, illustrations, and complex equations rendered as simple Python code, this book unlocks geometry in a way you can apply it to your daily work. About the technology Geometry is the core of game engines, computer-aided design, image-processing libraries, GIS, and much more.Understanding the mathematical underpinnings of tools and libraries empowers you to develop more efficient programming strategies.This unique guide gives you control over the geometry you need to deliver faster, cleaner results— and even build your own geometry tools!

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  • Mazes for Programmers
    Mazes for Programmers

    Unlock the secrets to creating random mazes! Whether you're a game developer, an algorithm connoisseur, or simply in search of a new puzzle, you're about to level up.Learn algorithms to randomly generate mazes in a variety of shapes, sizes, and dimensions.Bend them into Moebius strips, fold them into cubes, and wrap them around spheres.Stretch them into other dimensions, squeeze them into arbitrary outlines, and tile them in a dizzying variety of ways.From twelve little algorithms, you'll discover a vast reservoir of ideas and inspiration.From video games to movies, mazes are ubiquitous. Explore a dozen algorithms for generating these puzzles randomly, from Binary Tree to Eller's, each copiously illustrated and accompanied by working implementations in Ruby.You'll learn their pros and cons, and how to choose the right one for the job.You'll start by learning six maze algorithms and transition from making mazes on paper to writing programs that generate and draw them.You'll be introduced to Dijkstra's algorithm and see how it can help solve, analyze, and visualize mazes. Part 2 shows you how to constrain your mazes to different shapes and outlines, such as text, circles, hex and triangle grids, and more.You'll learn techniques for culling dead-ends, and for making your passages weave over and under each other.Part 3 looks at six more algorithms, taking it all to the next level.You'll learn how to build your mazes in multiple dimensions, and even on curved surfaces.Through it all, you'll discover yourself brimming with ideas, the best medicine for programmer's block, burn-out, and the grayest of days.By the time you're done, you'll be energized and full of maze-related possibilities!What You Need: The example code requires version 2 of the Ruby programming language.Some examples depend on the ChunkyPNG library to generate PNG images, and one chapter uses POV-Ray version 3.7 to render 3D graphics

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  • Beginning COBOL for Programmers
    Beginning COBOL for Programmers

    Beginning COBOL for Programmers is a comprehensive, sophisticated tutorial and modular skills reference on the COBOL programming language for established programmers.This book is for you if you are a developer who would like to—or must—add COBOL to your repertoire.Perhaps you recognize the opportunities presented by the current COBOL skills crisis, or are working in a mission critical enterprise which retains legacy COBOL applications.Whatever your situation, Beginning COBOL for Programmers meets your needs as an established programmer moving to COBOL. Beginning COBOL for Programmers includes comprehensive coverage of ANS 85 COBOL features and techniques, including control structures, condition names, sequential and direct access files, data redefinition, string handling, decimal arithmetic, subprograms, and the report writer.The final chapter includes a substantial introduction to object-oriented COBOL. Benefiting from over one hundred example programs, you’ll receive an extensive introduction to the core and advanced features of the COBOL language and will learn to apply these through comprehensive and varied exercises.If you've inherited some legacy COBOL, you’ll be able to grasp the COBOL idioms, understand the constructs, and recognize what's happening in the code you’re working with. Today’s enterprise application developers will find that COBOL skills open new—or old—doors, and this extensive COBOL reference is the book to help you acquire and develop your COBOL skills.

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  • Quantum Computing for Programmers
    Quantum Computing for Programmers

    This introduction to quantum computing from a classical programmer's perspective is meant for students and practitioners alike.Over 25 fundamental algorithms are explained with full mathematical derivations and classical code for simulation, using an open-source code base developed from the ground up in Python and C++.After presenting the basics of quantum computing, the author focuses on algorithms and the infrastructure to simulate them efficiently, beginning with quantum teleportation, superdense coding, and Deutsch-Jozsa.Coverage of advanced algorithms includes the quantum supremacy experiment, quantum Fourier transform, phase estimation, Shor's algorithm, Grover's algorithm with derivatives, quantum random walks, and the Solovay–Kitaev algorithm for gate approximation.Quantum simulation is explored with the variational quantum eigensolver, quantum approximate optimization, and the Max-Cut and Subset-Sum algorithms.The book also discusses issues around programmer productivity, quantum noise, error correction, and challenges for quantum programming languages, compilers, and tools, with a final section on compiler techniques for transpilation.

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  • Coderspeak : The Language of Computer Programmers
    Coderspeak : The Language of Computer Programmers


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  • OpenACC for Programmers : Concepts and Strategies
    OpenACC for Programmers : Concepts and Strategies

    The Complete Guide to OpenACC for Massively Parallel Programming Scientists and technical professionals can use OpenACC to leverage the immense power of modern GPUs without the complexity traditionally associated with programming them.OpenACCTM for Programmers is one of the first comprehensive and practical overviews of OpenACC for massively parallel programming. This book integrates contributions from 19 leading parallel-programming experts from academia, public research organizations, and industry.The authors and editors explain each key concept behind OpenACC, demonstrate how to use essential OpenACC development tools, and thoroughly explore each OpenACC feature set. Throughout, you’ll find realistic examples, hands-on exercises, and case studies showcasing the efficient use of OpenACC language constructs.You’ll discover how OpenACC’s language constructs can be translated to maximize application performance, and how its standard interface can target multiple platforms via widely used programming languages. Each chapter builds on what you’ve already learned, helping you build practical mastery one step at a time, whether you’re a GPU programmer, scientist, engineer, or student.All example code and exercise solutions are available for download at GitHub. Discover how OpenACC makes scalable parallel programming easier and more practicalWalk through the OpenACC spec and learn how OpenACC directive syntax is structuredGet productive with OpenACC code editors, compilers, debuggers, and performance analysis toolsBuild your first real-world OpenACC programsExploit loop-level parallelism in OpenACC, understand the levels of parallelism available, and maximize accuracy or performanceLearn how OpenACC programs are compiledMaster OpenACC programming best practicesOvercome common performance, portability, and interoperability challengesEfficiently distribute tasks across multiple processors Register your product at informit.com/register for convenient access to downloads, updates, and/or corrections as they become available.

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Similar search terms for Programmers:


  • Do freelance programmers earn more than employed programmers?

    Freelance programmers have the potential to earn more than employed programmers, as they have the ability to set their own rates and take on multiple projects simultaneously. However, freelance work can also be unpredictable and may not always provide a steady income. Employed programmers typically have a stable salary and benefits, but may not have the same earning potential as freelance programmers. Ultimately, the earning potential for both freelance and employed programmers can vary depending on factors such as experience, skill level, and the demand for their services in the market.

  • What replaces programmers?

    As technology advances, automation and artificial intelligence are increasingly replacing certain tasks that programmers traditionally performed. This includes tasks such as code generation, debugging, and even some aspects of software design. However, programmers are still essential for more complex problem-solving, creativity, and critical thinking in developing new software solutions. Therefore, while automation may replace some aspects of programming, programmers themselves are still needed for their unique skills and expertise.

  • Can programmers hack?

    Yes, programmers can hack. Hacking is the act of gaining unauthorized access to computer systems or networks, and programmers have the technical skills and knowledge to exploit vulnerabilities in software and systems. However, it's important to note that not all programmers engage in hacking activities, and the vast majority of programmers use their skills for legitimate and ethical purposes, such as developing software, creating websites, and maintaining computer systems.

  • To the programmers:

    As an AI language model, I can provide a response to the question. However, it's important to note that I am not a programmer myself, but rather a tool created by a team of developers. If you have specific questions or requests for programmers, I can certainly help generate code, provide explanations, or offer guidance on programming-related topics. Just let me know how I can assist you!

  • Are programmers attractive?

    Attraction is subjective and varies from person to person. Whether or not programmers are attractive is a matter of personal preference and cannot be generalized. Some people may find the intelligence, problem-solving skills, and creativity of programmers attractive, while others may not. Ultimately, attractiveness is not determined by one's profession, but by a combination of personality, values, and physical appearance.

  • How do prehistoric programmers from the 80s differ from today's programmers?

    Prehistoric programmers from the 80s worked with limited resources and technology, often writing code in low-level languages and dealing with hardware constraints. They had to be highly skilled in optimizing code for performance and memory usage. Today's programmers have access to more advanced tools, languages, and frameworks, allowing them to focus more on problem-solving and higher-level concepts. They also work in more collaborative and agile environments, with a greater emphasis on teamwork and communication. Overall, the role of a programmer has evolved to be more focused on creativity, problem-solving, and collaboration, rather than just technical skills.

  • How do programmers learn?

    Programmers learn through a combination of formal education, self-study, and practical experience. Many programmers start by learning the fundamentals of programming through courses or degree programs in computer science or related fields. They then continue to learn by working on real-world projects, collaborating with other programmers, and staying up to date with the latest technologies and best practices through online resources, books, and workshops. Continuous learning and adaptation are essential in the fast-evolving field of programming.

  • Will programmers become unemployed?

    While advancements in technology may automate some programming tasks, it is unlikely that programmers will become unemployed. The demand for skilled programmers is expected to remain high as technology continues to evolve and new applications are developed. Programmers will need to adapt and upskill to stay relevant in the industry, but their expertise will continue to be valuable in creating and maintaining software systems.

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