It is not a secret that Python is the preferred language for building AI models, but why is this if Python is NOT one of the fastest-performing languages?
Although Python may not offer the highest performance, it has several strengths that make it the default choice for AI development. Most articles about Python you can find will tell you these strengths, but I also added some key features that made me choose Python.
1. Python and C
One of Python’s most powerful features is that we can create C extension modules through the C API. In short, this means we can execute C functions inside our Python applications to optimize performance in compute-intensive tasks.
Since Python is mainly written in C language, the C API allows the translation of Python objects into C types and vice versa.
2. Python versatility
Develop AI models, data mining, data visualization, and more. Python flawlessly will automatize all kinds of activities during the data life cycle from start to end.
3. Dynamic typing
This means that the variable type is defined at run time, and you don’t need to declare it. In other languages like Java, you must declare the variable type before using it; in Python, the type is defined in the run time when a value is assigned. This is especially useful when dealing with huge numbers.
// Java
int var = 2;# Python
var = 24. Python’s Simplicity
Python’s syntax is very similar to natural language, making it easy to read and write; this means that the way you write code in Python is very close to the way you will write instructions for your deaf grandma, which is a significant advantage when you are starting to code or build AI models.
5. Python’s Comunity
Since Python, to date, is the most popular language according to the TIOBE index, it is relatively easy to find someone who had precisely the same problem, had already solved it, and posted the solution online. Even if you use AI to solve your coding problems, It performs better with popular languages since there’s more documentation, community discussion, and extensive usage patterns to draw from.
6. Python’s Libraries
Python’s AI libraries, such as scikit-learn, TensorFlow, Keras, and PyTorch are the default toolkit for AI development, not only for the reasons explained above but they are straightforward to use (mostly). Sure, you’ll happily train your model until TensorFlow throws an error 27 layers deep, and PyTorch politely suggests you Google a 200-word error message. But hey, when it works, it’s magical.