elegant scipy

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Elegant Scipy

Author : Juan Nunez-Iglesias
ISBN : 9781491922941
Genre : Computers
File Size : 57. 56 MB
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Welcome to Scientific Python and its community. If you’re a scientist who programs with Python, this practical guide not only teaches you the fundamental parts of SciPy and libraries related to it, but also gives you a taste for beautiful, easy-to-read code that you can use in practice. You’ll learn how to write elegant code that’s clear, concise, and efficient at executing the task at hand. Throughout the book, you’ll work with examples from the wider scientific Python ecosystem, using code that illustrates principles outlined in the book. Using actual scientific data, you’ll work on real-world problems with SciPy, NumPy, Pandas, scikit-image, and other Python libraries. Explore the NumPy array, the data structure that underlies numerical scientific computation Use quantile normalization to ensure that measurements fit a specific distribution Represent separate regions in an image with a Region Adjacency Graph Convert temporal or spatial data into frequency domain data with the Fast Fourier Transform Solve sparse matrix problems, including image segmentations, with SciPy’s sparse module Perform linear algebra by using SciPy packages Explore image alignment (registration) with SciPy’s optimize module Process large datasets with Python data streaming primitives and the Toolz library

Mastering Scipy

Author : Francisco J. Blanco-Silva
ISBN : 9781783984756
Genre : Computers
File Size : 43. 91 MB
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Implement state-of-the-art techniques to visualize solutions to challenging problems in scientific computing, with the use of the SciPy stack About This Book Master the theory and algorithms behind numerical recipes and how they can be applied to real-world problems Learn to combine the most appropriate built-in functions from the SciPy stack by understanding the connection between the sources of your problem, volume of data, or computer architecture A comprehensive coverage of all the mathematical techniques needed to solve the presented topics, with a discussion of the relevant algorithms built in the SciPy stack Who This Book Is For If you are a mathematician, engineer, or computer scientist with a proficiency in Python and familiarity with IPython, this is the book for you. Some basic knowledge of numerical methods in scientific computing would be helpful. What You Will Learn Master relevant algorithms used in symbolic or numerical mathematics to address approximation, interpolation, differentiation, integration, root-finding, and optimization of scalar or multi-variate functions Develop different algorithms and strategies to efficiently store and manipulate large matrices of data, in particular to solve systems of linear equations, or compute their eigenvalues/eigenvectors Understand how to model physical problems with systems of differential equations and distinguish the factors that dictate the strategies to solve them Perform statistical analysis, hypothesis test design and resolution, or data mining at a higher level, and apply them to real-life problems in the field of data analysis Gain insights on the power of distances, Delaunay triangulations and Voronoi diagrams for Computational Geometry, and apply them to various engineering problems Familiarize yourself with different techniques in signal/image processing, including filtering audio, images, or video to extract information, features, or remove components In Detail The SciPy stack is a collection of open source libraries of the powerful scripting language Python, together with its interactive shells. This environment offers a cutting-edge platform for numerical computation, programming, visualization and publishing, and is used by some of the world's leading mathematicians, scientists, and engineers. It works on any operating system that supports Python and is very easy to install, and completely free of charge! It can effectively transform into a data-processing and system-prototyping environment, directly rivalling MATLAB and Octave. This book goes beyond a mere description of the different built-in functions coded in the libraries from the SciPy stack. It presents you with a solid mathematical and computational background to help you identify the right tools for each problem in scientific computing and visualization. You will gain an insight into the best practices with numerical methods depending on the amount or type of data, properties of the mathematical tools employed, or computer architecture, among other factors. The book kicks off with a concise exploration of the basics of numerical linear algebra and graph theory for the treatment of problems that handle large data sets or matrices. In the subsequent chapters, you will delve into the depths of algorithms in symbolic algebra and numerical analysis to address modeling/simulation of various real-world problems with functions (through interpolation, approximation, or creation of systems of differential equations), and extract their representing features (zeros, extrema, integration or differentiation). Lastly, you will move on to advanced concepts of data analysis, image/signal processing, and computational geometry. Style and approach Packed with real-world examples, this book explores the mathematical techniques needed to solve the presented topics, and focuses on the algorithms built in the SciPy stack.

Scipy Recipes

Author : V Kishore Ayyadevara
ISBN : 9781788295819
Genre : Computers
File Size : 24. 40 MB
Format : PDF, Mobi
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Tackle the most sophisticated problems associated with scientific computing and data manipulation using SciPy Key Features Covers a wide range of data science tasks using SciPy, NumPy, pandas, and matplotlib Effective recipes on advanced scientific computations, statistics, data wrangling, data visualization, and more A must-have book if you're looking to solve your data-related problems using SciPy, on-the-go Book Description With the SciPy Stack, you get the power to effectively process, manipulate, and visualize your data using the popular Python language. Utilizing SciPy correctly can sometimes be a very tricky proposition. This book provides the right techniques so you can use SciPy to perform different data science tasks with ease. This book includes hands-on recipes for using the different components of the SciPy Stack such as NumPy, SciPy, matplotlib, and pandas, among others. You will use these libraries to solve real-world problems in linear algebra, numerical analysis, data visualization, and much more. The recipes included in the book will ensure you get a practical understanding not only of how a particular feature in SciPy Stack works, but also of its application to real-world problems. The independent nature of the recipes also ensure that you can pick up any one and learn about a particular feature of SciPy without reading through the other recipes, thus making the book a very handy and useful guide. What you will learn Get a solid foundation in scientific computing using Python Master common tasks related to SciPy and associated libraries such as NumPy, pandas, and matplotlib Perform mathematical operations such as linear algebra and work with the statistical and probability functions in SciPy Master advanced computing such as Discrete Fourier Transform and K-means with the SciPy Stack Implement data wrangling tasks efficiently using pandas Visualize your data through various graphs and charts using matplotlib Who this book is for Python developers, aspiring data scientists, and analysts who want to get started with scientific computing using Python will find this book an indispensable resource. If you want to learn how to manipulate and visualize your data using the SciPy Stack, this book will also help you. A basic understanding of Python programming is all you need to get started.

Scipy And Numpy

Author : Eli Bressert
ISBN : 9781449305468
Genre : Computers
File Size : 49. 29 MB
Format : PDF, Docs
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"Optimizing and boosting your Python programming"--Cover.

Learning Scipy For Numerical And Scientific Computing Second Edition

Author : Sergio J. Rojas G.
ISBN : 9781783987719
Genre : Computers
File Size : 62. 96 MB
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This book targets programmers and scientists who have basic Python knowledge and who are keen to perform scientific and numerical computations with SciPy.

Machine Learning With Tensorflow 1 X

Author : Quan Hua
ISBN : 9781786461988
Genre : Computers
File Size : 53. 75 MB
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Tackle common commercial machine learning problems with Google's TensorFlow 1.x library and build deployable solutions. About This Book Enter the new era of second-generation machine learning with Python with this practical and insightful guide Set up TensorFlow 1.x for actual industrial use, including high-performance setup aspects such as multi-GPU support Create pipelines for training and using applying classifiers using raw real-world data Who This Book Is For This book is for data scientists and researchers who are looking to either migrate from an existing machine learning library or jump into a machine learning platform headfirst. The book is also for software developers who wish to learn deep learning by example. Particular focus is placed on solving commercial deep learning problems from several industries using TensorFlow's unique features. No commercial domain knowledge is required, but familiarity with Python and matrix math is expected. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build deep neural networks using TensorFlow 1.x Cover key tasks such as clustering, sentiment analysis, and regression analysis using TensorFlow 1.x Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Learn how to use multiple GPUs for faster training using AWS In Detail Google's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x. Firstly, you'll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data flow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You'll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you'll implement a complete real-life production system from training to serving a deep learning model. As you advance you'll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you'll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim. By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment. Style and approach This comprehensive guide will enable you to understand the latest advances in machine learning and will empower you to implement this knowledge in your machine learning environment.

Practical Data Science Cookbook

Author : Prabhanjan Tattar
ISBN : 9781787123267
Genre : Computers
File Size : 61. 84 MB
Format : PDF, Mobi
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Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization

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