In recent years, machine learning has taken the high-tech world by the storm, and C++ for machine learning, along with Python, are some of the means for going from point A to B in this regard. What’s in store’ then? Well, there are many impressive use cases of machine learning, with industrial automation, fraud detection, and speech and image recognition topping the list.
Of course, many cool products are enhanced by machine learning, too. Oftentimes, they are designed by some cutting-edge brands, trendsetters, and head turners. Some of them include Amazon, IBM, Salesforce, Google, Pinterest, and Twitter. Filtering out spam, perfecting chatbots, improving search ranking and prediction systems as well as making accurate treatment recommendations for patients belong to some of the most interesting machine learning applications performed by those giants.
Of course, high-end or world-renowned brands are not the only ones who deal with machine learning in C++, Python, or another programming language. There are more common and down-to-earth applications, too, such as research and analytical projects of various scope.
If you have been thinking about what technology to choose for your next machine learning project: C++ or Python, here’s our short guide.
Table of contents:
- C++ for machine learning in brief
- Machine learning with C++ or with Python – comparison
- Choose the best technology for your machine learning project
C++ for machine learning in brief
C++ ML is one of the options but when we talk about machine learning, what often comes to mind is, in fact, Python. It is the huge popularity of this programming language – simple yet powerful and extremely easy to learn – that helped areas such as artificial intelligence, machine learning, or data science grow.
Python is extremely easy to start with, and powerful enough to continue developing with it in many cases, even demanding ones. But C++ has its place in this story, too. It may be very useful as often being called and regarded as more performant.
Oftentimes, when Python, usually more productive for higher-level programming, hits its performance limits and you come up against a brick wall, the only way out may be switching to C++.
Interestingly, sometimes this may even mean rewriting the whole code from the beginning.
Moreover, even though many libraries involved in coding within machine learning projects are Python ones, their core is, very often, written in C or C++. For this reason, any of these can be referred to as a C++ machine learning library.
Anyway, those advantages of C++ in terms of performance usually cannot be enjoyed by average programmers and are limited to more sophisticated use cases. Definitely, Python is the answer in the case of analysis or research purposes and applications. And in the case of developing new algorithms, C++ can be the right choice.
Machine learning with C++ vs Python – comparison
Without any doubt, C++ machine learning is a multifaceted issue. It is said that as for writing code for AI purposes, 90% of programmers’ time is spent in Python, whereas 99% of CPU (or processing) time is consumed in C or C++.
If we decide to use C++ in machine learning (e.g. with a Linear Algebra library), we may expect an impressive performance. C++ is more complex and has more pitfalls than Python, and writing code and debugging is more demanding and time-consuming in C++, although it can run much faster than Python.
In Python, even complex machine learning algorithms may be tested easily and fast, and a software developer may proceed with the workflow smoothly.
Also, as Python is extensively used in machine learning, a lot of contributions, like renowned ML models, can be partly reused and revamped, or serve as a reference for future projects. On the other hand, C++ is perhaps better utilized in projects involving embedded systems and robotics.
Choose the best technology for your machine learning project
As for machine learning using C++, it’s good to remember that although C++ may outperform Python in terms of speed, its learning curve is very steep. Sometimes a decade is not enough to get acquainted with its ins and outs fully.
For those hesitant, making use of both languages and taking advantage of them may be just the right option.
Within Python, there is a possibility to use C++ or C libraries (machine learning libraries implemented in C++ or C), and thus gain the latter’s performance without the need to write code with it. Some examples of the impressive number of Python libraries or Python-based ecosystems perfect for machine learning include NumPy, SciPy, Jupyter, Dask, Scikit-learn, Pandas, PyTorch and TensorFlow (with Keras API).
Whatever you choose, it’s certainly worth trying because machine learning indisputably belongs to the future. Also, it’s very useful and life-enhancing, and its applications also cover many down-to-earth use cases. They cover areas and solutions such as daily product recommendations, social media features, abnormal events and online fraud detection, industry automation and self-driving cars, traffic and commuting prediction, virtual shopping assistance, as well as speech and image recognition.
And to you, what technology or tools are best for machine learning projects? Is Python or C++ better? What kind of projects would you recommend them for?