IPython Interactive Computing and Visualization Cookbook


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IPython Interactive Computing and Visualization Cookbook
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Overview
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Sample Chapters
  • Leverage the new features of the IPython notebook for interactive web-based big data analysis and visualization
  • Become an expert in high-performance computing and visualization for data analysis and scientific modeling
  • A comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanations

Book Details

Language : English
Paperback : 423 pages [ 235mm x 191mm ]
Release Date : August 2014
ISBN : 1783284811
ISBN 13 : 9781783284818
Author(s) : Cyrille Rossant
Topics and Technologies : All Books, Application Development, Big Data and Business Intelligence, Open Source
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Cyrille Rossant

Dr. Cyrille Rossant is a French researcher in computational neuroscience. A graduate of the Ecole Normale Supérieure, Paris, where he studied Mathematics and Computer Science, he has also worked at Princeton University and University College London. He is interested in all kinds of relationships between brains and computers, including models of neural processing, high-performance simulations of neural networks, and analysis of neurophysiological data. He has also worked on parallel computing and high-performance visualization technologies for Python, and he is a core developer of Vispy, a visualization package. He is the author of Learning IPython for Interactive Computing and Data Visualization, Packt Publishing, the prequel of this cookbook.
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What you will learn from this book

  • Code better: write high-quality, readable and well-tested programs, profile and optimize your code, conduct reproducible interactie computing experiments.
  • Master all the new features of the IPython notebook, including the interactie HTML/JaaScript widgets
  • isualize big data with Matplotlib and OpenGL, and create interactie plots in the IPython notebook
  • Simulate deterministic and stochastic dynamical systems in Python
  • Analyze data with Bayesian or frequentist statistics (Pandas, PyMC, R), and learn from actual data through machine learning (scikit-learn)
  • Gain aluable insights into signals, images, audio processing with SciPy and scikits, and computer ision with OpenC
  • Learn how to write blazingly fast Python programs with NumPy, PyTables, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA and OpenCL), parallel IPython, MPI, and many more
  • Familiarize yourself with the math in Python with SymPy and Sage: algebra, analysis, logic, graphs, geometry, and probability theory

In Detail

There is today an increasing need for data scientists skilled in high-performance numerical computing. IPython and the rest of the scientific Python ecosystem around it form an extraordinary open platform in this respect, even competing with alternative commercial products. Notably, the IPython notebook offers a fantastic framework for sharing and diffusing your work in the form of rich and interactive Web documents.

The 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 features to the most advanced tricks to 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 statistical learning.

You will learn the most advanced interactive computing techniques after having reviewed the basics. Then, you are going to apply those to diverse scientific topics.

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

Approach

A one-stop guide for high-performance scientific computing and data science in Python. Full of hands-on, ready-to-use, highly focused recipes with diverse real-world examples and clear, detailed step-by-step explanations.

Who this book is for

Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, 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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