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Genetic Algorithms and Machine Learning for Programmers
Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning.Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters.Learn how to test your ML code and dive into even more advanced topics.If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes.Build a repertoire of algorithms, discovering terms and approaches that apply generally.Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions. Build genetic algorithms. Make nature-inspired swarms with ants, bees and particles. Create Monte Carlo simulations. Investigate cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection methods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems.Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer.See how the algorithms explore and learn by creating visualizations of each problem.Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas).Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray.These plotting and testing libraries are not required but their use will give you a fuller experience.Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.
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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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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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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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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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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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Learn to Program with Assembly : Foundational Learning for New Programmers
Many programmers have limited effectiveness because they don't have a deep understanding of how their computer actually works under the hood. In Learn to Program with Assembly, you will learn to program in assembly language - the language of the computer itself.Assembly language is often thought of as a difficult and arcane subject. However, author Jonathan Bartlett presents the material in a way that works just as well for first-time programmers as for long-time professionals. Whether this is your first programming book ever or you are a professional wanting to deepen your understanding of the computer you are working with, this book is for you. The book teaches 64-bit x86 assembly language running on the Linux operating system. However, even if you are not running Linux, a provided Docker image will allow you to use a Mac or Windows computer as well. The book starts with extremely simple programs to help you get your grounding, going steadily deeper with each chapter. At the end of the first section, you will be familiar with most of the basic instructions available on the processor that you will need for any task. The second part deals with interactions with the operating system. It shows how to make calls to the standard library, how to make direct system calls to the kernel, how to write your own library code, and how to work with memory. The third part shows how modern programming language features such as exception handling, object-oriented programming, and garbage collection work at the assembly language level. Additionally, the book comes with several appendices covering various topics such as running the debugger, vector processing, optimization principles, a list of common instructions, and other important subjects. This book is the 64-bit successor to Jonathan Bartlett's previousbook, Programming from the Ground Up, which has been a programming classic for more than 15 years. This book covers similar ground but with modern 64-bit processors, and also includes a lot more information about how high level programming language features are implemented in assembly language. What You Will LearnHow the processor operates How computers represent data internally How programs interact with the operating systemHow to write and use dynamic code librariesHow high-level programming languages implement their features Who This Book Is ForAnyone who wants to know how their computer really works under the hood, including first time programmers, students, and professionals.
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Programming Clojure : Pragmatic Programmers: 3rd Edition
Programming Clojure : Pragmatic Programmers: 3rd Edition
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