IPython Interactive Computing and Visualization Cookbook: Over 100 hands-on recipes to sharpen your skills in high-performance numerical computing & data scie
(eBook)

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Published
Packt Publishing, 2018.
Status
Available Online

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Format
eBook
Language
English
ISBN
9781785881930

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APA Citation, 7th Edition (style guide)

Cyrille Rossant., & Cyrille Rossant|AUTHOR. (2018). IPython Interactive Computing and Visualization Cookbook: Over 100 hands-on recipes to sharpen your skills in high-performance numerical computing & data scie . Packt Publishing.

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

Cyrille Rossant and Cyrille Rossant|AUTHOR. 2018. IPython Interactive Computing and Visualization Cookbook: Over 100 Hands-on Recipes to Sharpen Your Skills in High-performance Numerical Computing & Data Scie. Packt Publishing.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

Cyrille Rossant and Cyrille Rossant|AUTHOR. IPython Interactive Computing and Visualization Cookbook: Over 100 Hands-on Recipes to Sharpen Your Skills in High-performance Numerical Computing & Data Scie Packt Publishing, 2018.

MLA Citation, 9th Edition (style guide)

Cyrille Rossant, and Cyrille Rossant|AUTHOR. IPython Interactive Computing and Visualization Cookbook: Over 100 Hands-on Recipes to Sharpen Your Skills in High-performance Numerical Computing & Data Scie Packt Publishing, 2018.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

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Grouped Work IDa5dfeada-e68a-b547-8341-19ed357ce40f-eng
Full titleipython interactive computing and visualization cookbook over 100 hands on recipes to sharpen your skills in high performance numerical computing and data scie
Authorrossant cyrille
Grouping Categorybook
Last Update2023-04-05 18:45:54PM
Last Indexed2024-03-27 05:20:24AM

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First LoadedSep 7, 2023
Last UsedSep 7, 2023

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    [synopsis] => Learn to use IPython and Jupyter Notebook for your data analysis and visualization work

Key Features
Leverage the Jupyter Notebook for interactive data science and visualization
Become an expert in high-performance computing and visualization for data analysis and scientific modeling
Comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanations

Book Description
Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and constitute an ideal gateway to the platform.

This edition of IPython Interactive Computing and Visualization Cookbook contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning.

The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics.

What you will learn
Master all features of the Jupyter Notebook
Code better: write high-quality, readable, and well-tested programs; profile and optimize your code; and conduct reproducible interactive computing experiments
Visualize data and create interactive plots in the Jupyter Notebook
Write blazingly fast Python programs with NumPy, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA), parallel IPython, Dask, and more
Analyze data with Bayesian or frequentist statistics (Pandas, PyMC, and R), and learn from actual data through machine learning (scikit-learn)
Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCV
Simulate deterministic and stochastic dynamical systems in Python
Familiarize yourself with math in Python using SymPy and Sage: algebra, analysis, logic, graphs, geometry, and probability theory
Who This Book Is For
This book is for anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, and hobbyists. Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.
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