This is an old revision of the document!
In this lab, we will study a new library in python that offers fast, memory efficient manipulation of vectors, matrices and tensors: numpy. We will also study basic plotting of data using the most popular data visualization libraries in the python ecosystem: matplotlib.
For scientific computing we need an environment that is easy to use, and provides a couple of tools like manipulating data and visualizing results. Python is very easy to use, but the downside is that it's not fast at numerical computing. Luckily, we have very eficient libraries for all our use-cases.
Core computing libraries
* numpy and scipy: scientific computing * matplotlib: plotting library
Machine Learning
* sklearn: machine learning toolkit * tensorflow: deep learning framework developed by google * keras: deep learning framework on top of `tensorflow` for easier implementation * pytorch: deep learning framework developed by facebook
Statistics and data analysis
* pandas: very popular data analysis library * statsmodels: statistics
We also have advanced interactive environments:
* Ipython: advanced python console * Jupyter: notebooks in the browser
There are many more scientific libraries available.
For this lab, we will use Google Colab for exploring numpy and matplotlib. Please solve your tasks here by clicking โOpen in Colaboratoryโ.
You can then export this python notebook as a PDF (File โ Print) and upload it to Moodle.
Please take a minute to fill in the feedback form for this lab.