As data scientists and computer vision specialists, the most prominent tools we use are Matlab and Python. In the following blog post, I’d like to share with you some thoughts and best practices regarding the combination of these two important tools.
In recent years, Matlab lost a lot of its prestige and Python became much more popular. Nevertheless, I still find many advantages working with Matlab. Its IDE (Integrated Development Environment) is extremely convenient and allows me to debug and dig into my code very efficiently, more than any Python IDE allows. Since debugging and digging is the main action an algorithm developer is doing, this feature is very important to me.
I also found Matlab more convenient in visualization especially in 3D and the built-in functions are very stable with great documentation (after all, you do pay for something…).
From my experience, the main advantage in Python is the huge diversity of implementations of state-of-the-art algorithms. With millions of developers in this open-source code, I can be sure that if I need some implementation, someone has already done it. This is especially true in deep learning.
So, just like everything in life, this is not simply “black and white”. Both Matlab and Python have pros and cons. That’s why I was very happy to find out that Matlab can run very easily any Python command and package. How easily? All I had to do is write Py. followed by any Python command I chose. No need for imports or reinstallation of packages and so actually in some ways it is easier to run Python from Matlab than any other IDE!
So, if you think that both Matlab and Python are great tools, here is a great option to use them both, simultaneously.
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Asaf Shimshovitz, PhD