Setup And Libraries
Contents
Setup And Libraries¶
Course participants are expected to have their own laptops/PCs. We use Git as version control software and the usage of providers like GitHub, GitLab or similar are strongly recommended.
We will make extensive use of Python as programming language and its myriad of available libraries.
If you have Python installed and you feel pretty familiar with installing different packages, we recommend that you install the following Python packages via pip as
pip install numpy scipy matplotlib ipython scikit-learn mglearn sympy pandas pillow
For OSX users we recommend, after having installed Xcode, to install brew. Brew allows for a seamless installation of additional software via for example
brew install python3
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
sudo apt-get install python3
You will find Jupyter notebooks invaluable in your work. You can run Python code in the Jupyter notebooks, with the immediate benefit of visualizing your data. You can also use compiled languages like C++, Rust, Julia, Fortran etc if you prefer. The focus in these lectures will be mainly on Python.
To install and start running jupyter lab (the newest and best notebook interface), follow the instructions here.
Python installers¶
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
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.
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.
Furthermore, Google’s Colab:https://colab.research.google.com/notebooks/welcome.ipynb is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud. Try it out!
Useful Python libraries¶
Here we list several useful Python libraries we strongly recommend (if you use anaconda many of these are already there)
NumPy:https://www.numpy.org/ is a highly popular library for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays
The pandas:https://pandas.pydata.org/ library provides high-performance, easy-to-use data structures and data analysis tools
Xarray:http://xarray.pydata.org/en/stable/ is a Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun!
Scipy:https://www.scipy.org/ (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering.
Matplotlib:https://matplotlib.org/ is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.
Autograd:https://github.com/HIPS/autograd can automatically differentiate native Python and Numpy code. It can handle a large subset of Python’s features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives
SymPy:https://www.sympy.org/en/index.html is a Python library for symbolic mathematics.
scikit-learn:https://scikit-learn.org/stable/ has simple and efficient tools for machine learning, data mining and data analysis
TensorFlow:https://www.tensorflow.org/ is a Python library for fast numerical computing created and released by Google
Keras:https://keras.io/ is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano
And many more such as pytorch:https://pytorch.org/, Theano:https://pypi.org/project/Theano/ etc