The first tutorial is meant to introduce the basic image processing techniques that we are going to need during the course. In particular, we are going to demonstrate how convolution can be used to detect a variety of image features, then cover some related neural netowork building blocks.

Goals:

• Gain an understanding of digital image representations
• Learn about the convolution operation and its practical aspects
  
• Learn simple ways of manipulating images, such as linear filters.

Jupyter:

• The tutorials presented at the seminars all use Jupyter notebooks to display a combination of text, code and outputs in a well-structured and easily legible format. 
• Every tutorial file has the extension .ipynb, and cannot be opened with a text editor or most Python IDEs. Instead, you should use the jupyter-lab environment, which comes installed on every lab PC. 
• To start a Jupyter environment, either use a CLI and execute the jupyter-lab command (Windows or Unix), or run the command from the address bar of the file explorer (Windows only). In both cases, the Jupyter environment should open in the default browser automatically, with the latter method also setting the working directory to the directory where it was executed. 
• Jupyter notebooks are divided into "cells", which are individually runnable code snippets. When you run a cell, all variables and functions declared within that cell are added to a global workspace, potentially overwriting existing ones that had the same name. To run a cell, you can either click the run button on the top ribbon or press shift + enter.
• All the notebooks are designed in such a way that running every cell sequentially (i.e. one after the other and each only once) will give the desired output. If you go back to previous cells, it's possible for the notebook to reach an inconsistent state. In this case, you have to either fix the inconsistency by rerunning previous cells manually or restarting the kernel and stepping through the cells, or by pressing the dedicated button for running all cells on the top ribbon. 
• You can find further instructions and documentation at the following link: https://jupyterlab.readthedocs.io/en/latest/user/interface.html