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SW tools

This page summarizes installation and usage of several tools for machine learning and data mining.


For tutorials and homeworks we will be using (among others) python and jupyter notebooks. For convenience, we use conda distribution of Python. Download miniconda from https://conda.io/miniconda.html, run the installer and add miniconda to your path. Bellow see how to do this in Linux.


$ wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh # if you use linux, otherwise follow instructions for your OS
$ bash Miniconda3-latest-Linux-x86_64.sh
Do you wish the installer to prepend the Miniconda3 install location
to PATH in your /home/petr/.bashrc ? [yes|no]
[no] >>> yes

$ bash # reset your bash terminal
$ conda create -n SMU python=3
$ source activate SMU # on windows use activate SMU
$ conda install jupyter
$ conda install numpy

When installation is finished, you can try how to use the tools yourselves. A quickstart tutorial can be found on https://docs.scipy.org/doc/numpy-dev/user/quickstart.html. To see differences between numpy and MATLAB, visit https://docs.scipy.org/doc/numpy-dev/user/numpy-for-matlab-users.html.

In jupyter, you can use markdown language, see https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet. To run Jupyter, type


jupyter notebook
in your terminal and follow the instructions.

Use command


source activate SMU
each time when you want to use the environment and/or run Jupyter.

courses/smu/tutorials/tutorial1.txt · Last modified: 2020/02/11 16:46 (external edit)