Book Image

IPython Interactive Computing and Visualization Cookbook - Second Edition

By : Cyrille Rossant
Book Image

IPython Interactive Computing and Visualization Cookbook - Second Edition

By: Cyrille Rossant

Overview of this book

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 they constitute an ideal gateway to the platform. IPython Interactive Computing and Visualization Cookbook, Second Edition 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.
Table of Contents (19 chapters)
IPython Interactive Computing and Visualization CookbookSecond Edition
Contributors
Preface
Index

Introduction


In the previous chapter, we were interested in getting insight into data, understanding complex phenomena through partial observations, and making informed decisions in the presence of uncertainty. Here, we are still interested in analyzing and processing data using statistical tools. However, the goal is not necessarily to understand the data, but to learn from it.

Learning from data is close to what we do as humans. From our experience, we intuitively learn general facts and relations about the world, even if we don't fully understand their complexity. The increasing computational power of computers makes them able to learn from data too. That's the heart of machine learning, a branch of artificial intelligence at the intersection of computer science, statistics, and applied mathematics.

This chapter is a hands-on introduction to some of the most basic methods in machine learning. These methods are routinely used by data scientists. We will use these methods with scikit-learn...