Can You Do Machine Learning With C++?

Machine learning has established itself as one of the frontiers of computer science and technology in the modern era. The ability to program machines to learn and improve their performance over time has been extremely useful across multiple domains. But while machine learning is typically associated with languages like Python and R, is it possible to implement it using C++?

You can do machine learning with C++. Not only is it possible, but it is actually the industry standard for computationally demanding projects (since C++ is faster than Python) and for robotics (since C++ is great at software-hardware integration).

In this article, we will explore this subject further. We will also discuss why other languages like Python and R are generally favored over C++ when it comes to machine learning. Towards the end of the article, we will be sharing some of the best machine learning libraries available for C++.

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Introduction to Machine Learning in C++

Machine learning with C++ both has its advantages and disadvantages. There is a common misconception among beginners in machine learning that Python or R are more preferred over C++ when it comes to machine learning.

The truth is, machine learning is a vast interdisciplinary area that intersects with a lot of other domains that include data science. There are certain instances of machine learning for which you will want to employ Python or R, such as data mining or data analytics. But there are other areas of this domain, such as deep learning or computer vision, where it might be more efficient to use C++.

Another encouraging factor for doing machine learning in C++ is that it is much faster than Python or R. In fact, this is one of the reasons most professionals use C++ when programming a robot.

Python, however, is a much easier language to learn, and if you are trying to build simple applications like ones that predict sales or the weather, you might have an advantage sticking to it. But if you are someone who has already programmed a fair bit on C++, you may choose to continue with your machine learning journey in C++. You might actually have little advantage in terms of speed.

There are also plenty of large-scale machine learning frameworks implemented in C++ (including Caffe, TensorFlow, OpenNN, etc.). So rest assured, you will not be lost if you choose to do machine learning in C++.

A major disadvantage of working with C++ is that it is a relatively difficult language to write or debug. This will make the feedback cycle of the programming much more difficult. So unless you are implementing some heavy application that requires faster execution, it is generally considered safer and wiser to do machine learning in Python.

Why Are Other Languages Like Python More Popular for Machine Learning?

Python and R are, however, the industry-standard when it comes to machine learning in general. With Python, it has been an arbitrary choice, simply because it is a very readable language and also because it is easy for beginners to start programming in. This early adoption resulted in a lot of professionals continuing to work with the language, which, as of today, has resulted in a massive machine learning community/ecosystem.

Like we discussed earlier, C++ is actually faster than Python. But unless you are building computationally heavy applications like ones implementing deep learning, for the most part, you really won’t be able to tell the difference. And not just that, but it can actually take you much longer to code in C++. This is because C++ is a relatively difficult language to write and to debug.

As a professional, the time it takes you to build or update an application really matters, just as much as how long it takes the program to run or execute. And besides just the overall simplicity of Python, there is a huge repository of past machine learning implementations available for the language. This is because Python has been the industry-standard in machine learning, data science, and data analytics for a long time.

Thus it becomes a lot easier to simply reuse the past implementations instead of writing the code all over again and thus reinventing the wheel.

Furthermore, it is also worth noting that, for the most part, the idea that Python is slower than C++ is an overstatement. Unless you are working on some computationally demanding project like a deep learning project, the difference in terms of execution speed won’t really be noticeable.

Machine Learning Libraries for C++

In this section, we will be looking at some of the most popular machine learning libraries for C++. A lot of beginner programmers need to realize that much of the work you will be doing as professionals will be through the use of libraries. Libraries are important because they save you the time and effort of repeatedly writing the same complicated lines of code.

Here are some of the most popular machine learning libraries for C++:

• TensorFlow is one of the most popular machine learning libraries out there. It is an open-source library that is easy to modify, easy to integrate, and has plenty of community resources to help you along the way.

• mlpack is a machine learning library written in C++ that has been built upon the Armadillo linear algebra library. It can provide a fast and extensible implementation of a range of machine learning algorithms. It is available for five languages: C++, Python, Julia, Go, and CLI.

• OpenNN (Open Neural Networks) is one of the most popular C++ libraries for advanced analytics using neural networks, one of the most modern and successful machine learning techniques. It is an open-source library, and it features a great execution speed and optimal memory allocation. OpenNN is renowned within the Neural Net community for its efficiency.

• Armadillo is a unique entry on our list because it is not exactly a machine learning library. It is actually a linear algebra library. But it is one of the most popular C++ libraries out there, and its linear algebraic methods can be used to implement machine learning algorithms as well. Armadillo features easy syntax (that is very similar to MATLAB) and high-speed execution.

• Shark is an open-source C++ machine learning library that offers features such as modularity and speed. It comes with several machine learning implementations, from linear/non-linear optimization to neural nets. It is perfect for research as well as use in applications.  

• Caffe is short for Convolutional Architecture for Fast Feature Embedding, and it is one of the best deep learning frameworks out there. This framework was developed by the joint effort of The Berkeley Vision and Learning Center (BVLC)/Berkeley AI Research (BAIR) and community contributors. It features an expressive architecture, modular design, and high-speed execution. The community is pretty great too!

Conclusion

It is possible to implement machine learning algorithms in C++. In certain instances, such as when you have a computationally demanding program (like a deep learning application) or a hardware integration (like Robotics), C++ could actually be preferable to the industry standards such as Python. 

But Python offers a great advantage in terms of simplicity. You can start implementing complex algorithms after familiarizing yourself with some of the basic syntaxes.

C++ is actually a rather popular choice within the machine learning community. There are even plenty of machine learning libraries available for C++. So if you have a solid background in C++, you can consider doing machine learning in C++.

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