From 0cee2d9291d245bf06835a5da9f35c1e3a265824 Mon Sep 17 00:00:00 2001 From: mhjensen Date: Wed, 23 May 2018 16:59:37 -0400 Subject: [PATCH] added more text to intro chapters --- doc/src/How2ReadData/How2ReadData.do.txt | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/doc/src/How2ReadData/How2ReadData.do.txt b/doc/src/How2ReadData/How2ReadData.do.txt index c661123ff..ba4ff91ab 100644 --- a/doc/src/How2ReadData/How2ReadData.do.txt +++ b/doc/src/How2ReadData/How2ReadData.do.txt @@ -28,11 +28,15 @@ will consider is the so-called Ising model. Here we will use this model to produce data for selected spin configurations and attempt to classify the data. Finally, our last example consists of economic data from the OECD. +All these examples will serve us to get started, furthermore, they allow us to catch more than two birds with a stone. They will allow us to bring in some programming specific topics and tools as well as showing the power of various Python (and R) packages for machine learning and statistical data analysis. In the next chapter on linear algebra, we will also dive into more specific linear functions which are relevant for the various algorithms we will discuss. Here, we will mainly focus on two specific Python packages for Machine Learning, scikit-learn and tensorflow (see below for links etc). + + + !split ===== Software and needed installations ===== -We will make intensive use of python as programming language and the myriad of available libraries. +We will make intensive use of Python as programming language and its myriad of available libraries. Furthermore, you will find IPython/Jupyter notebooks invaluable in your work. You can run _R_ codes in the Jupyter/IPython notebooks, with the immediate benefit of visualizing your data. @@ -46,24 +50,24 @@ For OSX users we recommend also, after having installed Xcode, to install _brew_ for a seamless installation of additional software via for example o brew install python3 -For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution +For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution, you can use _pip_ as well and simply install Python as o sudo apt-get install python3 (or python for pyhton2.7) etc etc. !split ===== Python installers ===== -If you don't want to perform these operations separately, we recommend two widely used distrubutions which set up +If you don't want to perform these operations separately and venture into the hassle of exploring how to set up dependencies and paths, we recommend two widely used distrubutions which set up all relevant dependencies for Python, namely -o "Anaconda":"https://docs.anaconda.com/" Anaconda is an open source distribution of the Python and R programming languages for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system _conda_ +o "Anaconda":"https://docs.anaconda.com/", which is an open source distribution of the Python and R programming languages for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system _conda_ o "Enthought canopy":"https://www.enthought.com/product/canopy/" is a Python distribution for scientific and analytic computing distribution and analysis environment, available for free and under a commercial license. !split ===== Installing R, C++, cython or Julia ===== -You will also find it convenient to utilize R. -Jupyter/Ipython notebook allows you run _R_ code interactively in your browser. The software library _R_ is +You will also find it convenient to utilize R. Although we will mainly use Python during lectures and in various projects and exercises, we provide a full R set of codes for the same examples. Those of you already familiar with R should feel free to continue using R, keeping however an eye on the parallel Python set ups. Similarly, if you are a Python afecionado, feel free to explore R as well. +Jupyter/Ipython notebook allows you to run _R_ codes interactively in your browser. The software library _R_ is tuned to statistically analysis and allows for an easy usage of the tools we will discuss in these texts. To install _R_ with Jupyter notebook "following the link here":"https://mpacer.org/maths/r-kernel-for-ipython-notebook" @@ -79,7 +83,8 @@ interactively in the browser. Since we will emphasize writing many of the algori either Python or C++ as programming languages. To add more entropy, _cython_ can also be used when running your notebooks. It means that Python with the Jupyter/IPython notebook -setup allows you to integrate widely popular softwares and tools for scientific computing. With its versatility, +setup allows you to integrate widely popular softwares and tools for scientific computing. Similarly, the Numba Python package delivers increased performance capabilities with minimal rewrites of your codes. +With its versatility, including symbolic operations, Python offers a unique computational environment. Your Jupyter/IPython notebook can easily be converted into a nicely rendered _PDF_ file or a Latex file for further processing. For example, convert to latex as !bc pycod