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update on getting started

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mhjensen
2019-08-14 17:13:58 +02:00
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doc/src/How2ReadData/How2ReadData.do.txt
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@@ -22,7 +22,7 @@ polynomials with random noise added. We will use the Python
software package "Scikit-Learn":"http://scikit-learn.org/stable/" and
introduce various machine learning algorithms to make fits of
the data and predictions. We move thereafter to more interesting
cases such as nuclear binding energies.
cases such as data from say experiments (below we will look at experimental nuclear binding energies as an example).
These are examples where we can easily set up the data and
then use machine learning algorithms included in for example
_Scikit-Learn_.
@@ -31,7 +31,7 @@ These examples will serve us the purpose of getting
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
packages for machine learning and statistical data analysis.
libraries for machine learning and statistical data analysis.
Here, we will mainly focus on two
specific Python packages for Machine Learning, Scikit-Learn and
@@ -226,14 +226,14 @@ Here we list several useful Python libraries we strongly recommend (if you use a
===== Installing R, C++, cython or Julia =====
You will also find it convenient to utilize _R_. We will mainly
use Python during lectures and in various projects and exercises.
use Python during our lectures and in various projects and exercises.
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
browser. The software library _R_ is really tailored for statistical data analysis
and allows for an easy usage of the tools and algorithms we will discuss in these
lectures.
To install _R_ with Jupyter notebook
@@ -276,7 +276,7 @@ formats, ipython notebooks, latex files, pdf files etc with minimal edits. These
===== Numpy examples and Important Matrix and vector handling packages =====
There are several central software packages for linear algebra and eigenvalue problems. Several of the more
There are several central software libraries for linear algebra and eigenvalue problems. Several of the more
popular ones have been wrapped into ofter software packages like those from the widely used text _Numerical Recipes_. The original source codes in many of the available packages are often taken from the widely used
software package LAPACK, which follows two other popular packages
developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly here.
@@ -305,10 +305,8 @@ developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly he
\end{bmatrix}
\]
!et
!eblock
===== Basic Matrix Features =====
!bblock
The inverse of a matrix is defined by
@@ -317,13 +315,8 @@ The inverse of a matrix is defined by
\mathbf{A}^{-1} \cdot \mathbf{A} = I
\]
!et
!eblock
===== Basic Matrix Features =====
!bblock Matrix Properties Reminder
|----------------------------------------------------------------------|
| Relations | Name | matrix elements |
|----------------------------------------------------------------------|
@@ -337,7 +330,7 @@ The inverse of a matrix is defined by
!eblock
===== Some famous Matrices =====
=== Some famous Matrices ===
* Diagonal if $a_{ij}=0$ for $i\ne j$
* Upper triangular if $a_{ij}=0$ for $i > j$
@@ -350,7 +343,7 @@ The inverse of a matrix is defined by
* Banded, block upper triangular, block lower triangular....
===== Basic Matrix Features =====
=== More Basic Matrix Features ===
!bblock Some Equivalent Statements
For an $N\times N$ matrix $\mathbf{A}$ the following properties are all equivalent