How to start?
[If you are looking for the Python, Pycharm & packages installation guide, it’s here.]
If you are in this page it means that you want to start learning computer vision!
The goal of this site is that you will be able to learn how to implement computer vision algorithms, and also learn the math behind it, because after all- AI is Math.
How to start learning?
Starting here is easy.
Each chapter of the course has it own slides, accompanied with .ipynb (known as ipython notebooks or jupyter notebooks). Jupyter notebook is an open-source web application that allows you to view and run python files (and others) that contain live code, equations, visualizations and more- no need to install anything!
If you also want to run the selected notebook- simply search for this botton () at the top of every notebook.
If you want to investigate further or debug your code, here is how to install Python, Pycharm & packages. This is especially recommended if you want to try the exercises, since you’ll probably want to debug them without heavy use of print statements.
What is this course about?
The full table of contents of the course can be found here.
Are there any prerequisites to the course?
Established math proficiency.
- This course doesn’t assume any knowledge in image/signal processing- basic undergrad algabra course is sufficient.
- It does assume that the reader knows his way around seemingly hard math (never be afraid of math).
- Python knowledge is an advantage, but if you can deal with the math you can deal with the code. Matlab is deprecated… If you are reading this, this course is a great time to switch to Python.
Just give me the code. I don’t need to know the math…
Wrong. Code can change in time. Once, all algorithms were written in MATLAB, today in Python, and tomorrow- who knows? One thing that is kept the same is the math behind it all. Once you have a solid mathematical background of the subject, you will be able to do anything!