C++ is one of the oldest object-oriented programming languages. Bjarne Stroustrup built this language to fill the void of the C programming language. A few years after the launch, C++ became a very popular programming language, and still, millions of developers are using it for their builds. So, the question is: Is C++ Used in Machine Learning?
C++ is used in Machine Learning. In fact, C++ is the second most widely used programming language in Machine Learning. Not only in ML, but C++ is also being used to develop projects that include Artificial Intelligence and Robot Locomotives.
Stick with me for the rest of the article and learn more about why and how C++ is used in ML, Trust me! It is worth your time.
3 Reasons Why C++ is still used in Machine Learning
Though C++ isn’t the ideal programming language for Machine Learning, it has some features that make C++ capable enough to compete with modern programming languages like Python.
You might be familiar with the fact that C++ is a static programming language. If you don’t know what a static programming language is, then let me brief you about it;
Static programming language is a language that allocates memory while compile-time and performs all the primary operations at compile time.
Since C++ is a statically typed programming language, it offers efficient and quick RunTime compared to modern programming languages like Python and other dynamic programming languages.
Besides this, C++ won’t pop type errors during RunTime, which makes it a neat and time-efficient programming language.
In terms of performance, C++ sometimes outperforms Python, and this is why C++ is the second most widely used programming language in Machine Learning.
If you’ll ask a programmer, he/she says that Python is the easiest yet powerful programming language. Even if you ask me, I’ll say something similar for Python. No doubt, its simplicity is the reason Why Python is the top-rated programming language right now.
But, What about the second most straightforward programming? Is it Java? No. It’s C++. According to the report published by Vision Mobile in State of the Developer Nation Q1 2017, Over 43% of developers prefer C++ for its simplicity and efficiency in Machine Learning.
All of us are familiar with the age of C++, it’s quite an old programming language, and every graduate learns it in his/her institute. C + + has been in the market for more than three decades. Everyone who is into development at least persists in some knowledge about C++.
The popularity of C++ makes it a very common programming language and universal too. This allows beginners of the Machine Learning Industry to create projects using C++.
Fun Fact: C++ is mostly preferred while building Chatbots in Machine Learning, thanks to its secure architecture. – Vision Mobile.
Highly Recommended Article: Is Python Enough for Machine Learning?
C++ Machine Learning Libraries
Since you have asked whether C++ is used in Machine Learning or not, it indicates that you are planning to start developing Machine Learning projects using your C++ knowledge.
Before you proceed, let me tell you that, Machine Learning is a field that requires complex programming, and you might have to write thousands of lines of code to create a simple ML application.
This sounds tiring, Right!
But What if I tell you that some C++ experts have already built dozens of libraries of C++ that make its implementation in ML relatively easy.
You can use those pre-built libraries to make your project less tiring and much more efficient. In this, I’ll talk about two very popular libraries of C++:
- MLPack Library
- Shark Library
MLPack is an open-source Machine Learning library that contains the following algorithms:
- Python Binding
- Julia Binding
- Command-Line Programs
MLpack is preferred because it provides fast and scalable solutions that can easily be implemented into large-scale Machine Learning applications. MLPack library is mainly used for its algorithms.
Note: To learn more about the MLpack library, you can refer to its official website.
Shark Library was built to provide algorithms for Supervised Learning like:
- Neural Network
- Linear Regression
Apart from this, Shark Library also provides support for Linear Algebra and Numerical Optimization.
Shark is preferred because of its modular architecture and comprehensive support for different Mathematical related tasks.
Note: Refer to Shark’s official website for documentations.
Different Fields where C++ is still used
Machine Learning is not the only advanced field where C++ is used; there are several others:
Artificial Intelligence is a vast domain, and C++ can’t fulfill all its requirements. C++ is used explicitly in AI Game Development. According to the Vision Mobile report, 29% of developers prefer using C++ while developing AI games.
Robot Locomotion is a field where C++ is used in 27% of projects. C++ is preferred because it’s a static programming language and doesn’t pop the RunTime error, which allows the developers to debug their Robot Locomotion project more effectively.
As mentioned above, C++ has robust architecture, and that makes it entirely attack-proof. After Python, C++ is among the most secure programming languages, and that’s why some of the Network Security projects are developed using C++ language. Right now, 26% of Network Security projects have C++ as their primary language.
Though C++ is quite an old programming language compared with modern programming languages like Python, C++ still has the capabilities to fulfill the requirements of modern industries like Machine Learning and Data Analysis. It is still among the most actively used programming in the ML industry.
Thus, if you plan to make a career in Machine Learning and don’t have enough knowledge of languages like Python, you can still reach the pinnacle of your career using C++ as your primary programming language.
However, it is recommended to learn other programming languages to get compatible enough to work on any Machine Learning projects. You can check platforms like Udemy, Coursera, Edx to get free access to courses taught by industry experts and professionals. A few courses and several hours of practice will make you an expert in any programming language.