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Scientific_Computing_in_Python.pdf

Table of Contents Getting Started Variables and Basic Types Container Types Linear Algebra – Arrays Advanced Array Concepts Plotting Functions Classes Iterating Error Handling Namespaces, Scopes, and Modules Input and Output Testing Comprehensive Examples Symbolic Computations - SymPy References

Explore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda Accelerate Key Features Understand effective synchronization strategies for faster processing using GPUs Write parallel processing scripts with PyCuda and PyOpenCL Learn to use the CUDA libraries like CuDNN for deep learning on GPUs Book Description GPUs are proving to be excellent general purpose-parallel computing solutions for high performance tasks such as deep learning and scientific computing. This book will be your guide to getting started with GPU computing. It will start with introducing GPU computing and explain the architecture and programming models for GPUs. You will learn, by example, how to perform GPU programming with Python, and you'll look at using integrations such as PyCUDA, PyOpenCL, CuPy and Numba with Anaconda for various tasks such as machine learning and data mining. Going further, you will get to grips with GPU work flows, management, and deployment using modern containerization solutions. Toward the end of the book, you will get familiar with the principles of distributed computing for training machine learning models and enhancing efficiency and performance. By the end of this book, you will be able to set up a GPU ecosystem for running complex applications and data models that demand great processing capabilities, and be able to efficiently manage memory to compute your application effectively and quickly.

Scientific Computing with Python 3 English | 23 Dec. 2016 | ISBN: 1786463512 | 332 Pages | AZW3/MOBI/EPUB/PDF (conv) | 17.95 MB Key Features Your ultimate resource for getting up and running with Python numerical computations Explore numerical computing and mathematical libraries using Python 3.x code with SciPy and NumPy modules A hands-on guide to implementing mathematics with Python, with complete coverage of all the key concepts Book Description Python can be used for more than just general-purpose programming. It is a free, open source language and environment that has tremendous potential for use within the domain of scientific computing. This book presents Python in tight connection with mathematical applications and demonstrates how to use various concepts in Python for computing purposes, including examples with the latest version of Python 3. Python is an effective tool to use when coupling scientific computing and mathematics and this book will teach you how to use it for linear algebra, arrays, plotting, iterating, functions, polynomials, and much more. What you will learn The principal syntactical elements of Python The most important and basic types in Python The essential building blocks of computational mathematics, linear algebra, and related Python objects Plot in Python using matplotlib to create high quality figures and graphics to draw and visualize your results Define and use functions and learn to treat them as objects How and when to correctly apply object-oriented programming for scientific computing in Python Handle exceptions, which are an important part of writing reliable and usable code Two aspects of testing for scientific programming: Manual and Automatic About the Author Claus Fuhrer is a professor of scientific computations at Lund University, Sweden. He has an extensive teaching record that includes intensive programming courses in numerical analysis and engineering mathematics across various levels in many different countries and teaching environments. Claus also develops numerical software in research collaboration with industry and received Lund University's Faculty of Engineering Best Teacher Award in 2016. Jan Erik Solem is a Python enthusiast, former associate professor, and currently the CEO of Mapillary, a street imagery computer vision company. He has previously worked as a face recognition expert, founder and CTO of Polar Rose, and computer vision team leader at Apple. Jan is a World Economic Forum technology pioneer and won the Best Nordic Thesis Award 2005-2006 for his dissertation on image analysis and pattern recognition. He is also the author of "Programming Computer Vision with Python" (O'Reilly 2012). Olivier Verdier began using Python for scientific computing back in 2007 and received a PhD in mathematics from Lund University in 2009. He has held post-doctoral positions in Cologne, Trondheim, Bergen, and Umea and is now an associate professor of mathematics at Bergen University College, Norway. Table of Contents Getting Started Variables and Basic Types Container Types Linear Algebra – Arrays Advanced Array Concepts Plotting Functions Classes Iterating Error Handling Namespaces, Scopes, and Modules Input and Output Testing Comprehensive Examples Symbolic Computations - SymPy References

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