Search results for "Master Multi-Paradigm Python Programming"
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R 580.00
Large Scale Machine Learning with Python
Learn to build powerful machine learning models quickly and deploy large-scale predictive applicationsAbout This Book• Design, engineer and deploy scalable machine learning solutions with the power of Python• Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework • Build state-of-the-art models and develop personalized recommendations to perform machine learning at scaleWho This Book Is ForThis book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful.What You Will Learn• Apply the most scalable machine learning algorithms • Work with modern state-of-the-art large-scale machine learning techniques• Increase predictive accuracy with deep learning and scalable data-handling techniques• Improve your work by combining the MapReduce framework with Spark• Build powerful ensembles at scale• Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machineIn DetailLarge Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.Style and approachThis efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly.Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production. This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.
R 1,889.00
R 658.00
Learn Quantum Computing with Python and IBM Quantum
A step-by-step guide to working with programs that exploit quantum computing principles, with the help of IBM Quantum, Qiskit, and PythonKey FeaturesUnderstand the difference between classical computers and quantum computersWork with key quantum computational principles such as superposition, interference, and entanglement, and observe how they are leveraged on the IBM Quantum systemsRun your own quantum experiments and applications by integrating with Qiskit and PythonBook DescriptionThe IBM Quantum Platform was built to enable developers to learn the basics of quantum computing by providing access to high performant quantum computers and provide documentation and courses to help get up to speed with the latest features in quantum computing. Updated with new examples and changes to the platform, this edition begins with an introduction to the IBM Quantum Platform and the Quantum Information Science Kit (Qiskit) SDK. You will become well versed in the IBM Quantum Composer interface as well as running your quantum circuits on a real quantum computer. Along the way, you’ll learn some of the fundamental principles regarding quantum mechanics, qubits, quantum gates, quantum circuits, and the error mitigation techniques that are used to perform operations on qubits. As you build on your knowledge, you’ll understand the functionality of IBM Qiskit and the developer-focused resources so you can create your own quantum algorithms. You’ll learn how to monitor and optimize your quantum circuits. Lastly, you’ll look at the fundamental quantum algorithms and understand how they can be applied effectively. By the end of this quantum computing book, you'll know how to build quantum algorithms and will have gained a practical understanding of quantum computation that you can apply to your research or business. What you will learnGet familiar with the features within the IBM Quantum PlatformCreate and visualize quantum gates and circuitsOperate quantum gates on qubits using the IBM Quantum ComposerInstall and run your quantum circuits on an IBM Quantum computerDiscover Qiskit and its many features such as the Qiskit RuntimeGet to grips with fundamental quantum algorithms and error mitigation techniques to help you get startedUnderstand the new era of quantum utility and how this moves us closer towards quantum advantageWho this book is forThis book is for Python developers who are looking to learn quantum computing from the ground up and put their knowledge to use in practical situations with the help of the IBM Quantum Platform and Qiskit. Some background in computer science and high school-level physics and math is required.
R 1,465.00
Professional CUDA C Programming
Break into the powerful world of parallel GPU programming with this down-to-earth, practical guide Designed for professionals across multiple industrial sectors, Professional CUDA C Programming presents CUDA -- a parallel computing platform and programming model designed to ease the development of GPU programming -- fundamentals in an easy-to-follow format, and teaches readers how to think in parallel and implement parallel algorithms on GPUs. Each chapter covers a specific topic, and includes workable examples that demonstrate the development process, allowing readers to explore both the "hard" and "soft" aspects of GPU programming. Computing architectures are experiencing a fundamental shift toward scalable parallel computing motivated by application requirements in industry and science. This book demonstrates the challenges of efficiently utilizing compute resources at peak performance, presents modern techniques for tackling these challenges, while increasing accessibility for professionals who are not necessarily parallel programming experts. The CUDA programming model and tools empower developers to write high-performance applications on a scalable, parallel computing platform: the GPU. However, CUDA itself can be difficult to learn without extensive programming experience. Recognized CUDA authorities John Cheng, Max Grossman, and Ty McKercher guide readers through essential GPU programming skills and best practices in Professional CUDA C Programming, including: CUDA Programming ModelGPU Execution ModelGPU Memory modelStreams, Event and ConcurrencyMulti-GPU ProgrammingCUDA Domain-Specific LibrariesProfiling and Performance Tuning The book makes complex CUDA concepts easy to understand for anyone with knowledge of basic software development with exercises designed to be both readable and high-performance. For the professional seeking entrance to parallel computing and the high-performance computing community, Professional CUDA C Programming is an invaluable resource, with the most current information available on the market.
R 1,368.00
R 672.00
Paradigms for Fast Parallel Approximability
Various problems in computer science are 'hard', that is NP-complete, and so not realistically computable; thus in order to solve them they have to be approximated. This book is a survey of the basic techniques for approximating combinatorial problems using parallel algorithms. Its core is a collection of techniques that can be used to provide parallel approximations for a wide range of problems (for example, flows, coverings, matchings, travelling salesman problems, graphs), but in order to make the book reasonably self-contained, the authors provide an introductory chapter containing the basic definitions and results. A final chapter deals with problems that cannot be approximated, and the book is ended by an appendix that gives a convenient summary of the problems described in the book. This is an up-to-date reference for research workers in the area of algorithms, but it can also be used for graduate courses in the subject.
R 2,017.74
