Machine learning is one of the most difficult software development niches, as it requires high expertise, an extensive list of hard skills from developers, and certain requirements for choosing software tools for implementing algorithms based on it. In particular, we are talking about the choice of programming language. In turn, C++ is one of the few languages that can meet the requirements of ML algorithms for reliability and high performance.
As for additional tools that can benefit software engineers, we should mention machine learning libraries for C++ that significantly reduce development time by taking on the tasks of building complex computational algorithms. This tool allows engineers to focus their main efforts on building business logic. That's why we decided to dedicate this article to fourteen of the best libraries that can make an invaluable contribution to the development process of your ML project.
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How to Use C++ for Machine Learning
It is no secret that today the Python programming language is most often used for machine learning, since numerous tools have been developed to provide ML functionality. And even if developers are used to writing in a different programming language, when a task arises related to machine learning, they will most likely give preference to Python. However, in some cases, Python may not be fast enough. And then C++ comes to the rescue.
C++, along with Python, is one of the few programming languages that can be used to work on ML projects. It's all about its high speed of execution and reliability, two parameters that most languages lack.
At the same time, C++ may be the best choice for your project if Python is not productive enough when benchmarking test scripts or if the required library in this language does not yet exist. C++ also provides greater control over memory usage, which is an additional benefit in projects when this parameter is limited.
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Top 14 C++ Machine Learning Libraries
Now let's get down to fourteen of the best machine learning libraries for C++ that can greatly streamline the process of developing your digital solution.
TensorFlow is probably the best C++ machine learning library that allows developers to train artificial intelligence to solve different problems. The library was originally developed for Python but also has an implementation for C++.
Google developed it as an extension of the company's internal library DistBelief. TensorFlow is free, open source, available on GitHub, and actively supported by a community of enthusiasts.
In TensorFlow, training models are represented in graphs that help to effectively solve "route" problems when creating neural networks. TensorFlow also works with tensors — multidimensional data structures in vector space. They describe the paths of a graph whose vertices are mathematical operations.
TensorFlow is well-suited for automated image annotation in systems like DeepDream. As for the mother company of this library, Google uses it to increase the relevance of Google's SERP rankings, at the heart of the RankBrain tool. TensorFlow is also ideal for training Generative Adversarial Networks (GANs).
Caffe is a deep machine learning library in C++ designed by the Berkeley AI Research (BAIR), Learning Center (BVLC), and their communities to ensure increased speed and modularity.
This project has a fairly low entry threshold thanks to high-quality tutorials and lectures from Caffe Summer Bootcamp. Additionally, this is facilitated by the absence of the need to use programming (at least for implementing simple tasks). Configurations with Caffe can be made through configuration files, and the library itself is launched from the command line.
From a practical point of view, Caffe is very fast as it uses the GPU (although you can get by with the CPU). This C++ library for machine learning supports many types of machine learning aimed at solving image classification and segmentation problems. Caffe provides convolutional neural networks, RCNNs, long short-term memory networks, and fully connected neural networks.
Microsoft Cognitive Toolkit (CNTK)
The Microsoft Cognitive Toolkit (CNTK) is an open-source solution for distributed deep learning. Based on its GitHub star rating, today, it is the third most popular specialized ML tool after TensorFlow and Caffe. In particular, CNTK is faster than TensorFlow, and in recurrent networks, it gives up to a five- or ten-fold performance gain. In addition, this tool demonstrates one of the highest accuracies for training deep learning models and has a flexible and powerful API for C++.
CNTK describes neural networks as a series of computational steps using a directed graph. It allows data science engineers to easily implement and integrate popular types of models such as redirected DNNs, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs/LSTMs). Also, CNTK implements stochastic gradient descent learning with automatic differentiation and parallelization across multiple GPUs and servers.
Armadillo is a C++ ML library for scientific computing with high-level syntax and functionality similar to Matlab. It can also be used to quickly convert code into a production environment.
This library features classes for vectors, matrices, and cubes, a sophisticated expression parser, dynamic evaluation based on detected matrix structures, multiple matrix decompositions, OpenMP multithreading, and more. All these advantages make this project an ideal choice for implementing solutions based on machine learning, pattern recognition, computer vision (you can also learn more about how to use machine learning in the medical field), signal processing, statistics, etc.
mlpack is another popular machine learning C++ library built on top of the linear algebra library Armadillo with a narrow focus on scalability, speed, and ease of use. Its goal is to make machine learning possible for novice users with a simple and consistent API.
This is achieved through command line executables that can be used as black boxes and a modular API for advanced developers that allows them to make changes to internal algorithms quickly. This library is perfect for implementing collaborative filtering, density estimation trees, Euclidean minimum spanning trees, GMMs, K-Means clustering, tree-based range search, etc. Also, it supports porting program code into Python projects.
DyNet is a C++ library with Python bindings specifically designed for building neural networks with a complex structure or control flow. In particular, it performs well in the processing of tree or graph structures, reinforcement learning, or learning with exploration.
Some of the most popular scenarios in which this library can be used are syntactic parsing, machine translation, speech recognition, graph parsing, language modeling, tagging, and morphology (in particular, in morphological inflection generation and inflection generation with hard attention). It can work both on CPU and GPU.
Shogun is a free solution with multiple machine-learning features and a focus on Support Vector Machines (SVMs). Unlike other ML libraries in C++ from our list, this project focuses on kernel-based machines for solving classification and regression problems. One of the main advantages of this library is the well-researched documentation and strong community (although the library has developed very slowly since its launch back in 1999). Along with this, working with the API in Shogun is quite difficult.
Fast Artificial Neural Network (FANN) is an open-source neural network library written in C language (also supports C++). The library implements multilayer artificial neural networks with support for fully and sparsely connected networks. It's easy to use, versatile, well-documented, and fast. Key features of FANN include backpropagation learning, evolving topology learning, cross-platform, and support for both floating and fixed point numbers.
Since its first launch in 2003, FANN has been widely used in solutions for artificial intelligence, aerospace engineering, genetics, image recognition, and so on.
Facebook AI Similarity Search (FAISS) is another great library that allows ML engineers to find similar multimedia documents quickly. This project does a great job with datasets of billions of examples and runs about 8.5 times faster than the fastest k-sampling algorithm running on GPU.
Written in C++, OpenNN is an open-source neural network library for advanced analytics. The library contains complex algorithms and utilities for working with artificial intelligence solutions, such as classification, regression, prediction, and others. The main advantage is high performance (provided primarily by multiprocessor programming).
The library is often used for research purposes, as it allows ML engineers to implement any number of levels of non-linear processors for supervised learning.
PyTorch is one of the latest deep learning libraries with a C++ frontend built by the Facebook team. Unlike other popular libraries such as Caffe and TensorFlow it is actively used to solve problems such as computer vision and natural language processing.
There is a whole ecosystem around this C++ machine-learning framework. It consists of various libraries developed by third-party teams: in particular, we are talking about PyTorch Lightning and Fast.ai for simplifying the process of training models, Pyro, a module for probabilistic programming, Flair, for natural language processing, and Catalyst, for training DL and RL models. PyTorch is also well known for its advanced GPU-accelerated tensor calculation and deep neural networks built on a tape-based autograd system.
SHARK is a set of several open-source C++ machine learning libraries that allows ML experts to implement linear and non-linear optimization, neural networks, supervised and unsupervised learning, evolutionary algorithms, and more. It also provides linear and non-linear optimization methods, kernel-based learning algorithms, neural networks, and various machine learning methods. It serves as a powerful toolkit for real-world applications and can also be used for research purposes.
Scikit-Learn is a NumPy- and SciPy-based library that can be actively used in data preprocessing and data modeling projects involving supervised and unsupervised learning algorithms. It has an actively developing community where anyone can find answers to any questions.
As for specific use cases for Scikit-Learn, these could be decision trees, linear regression, logistic regression, classification, clustering, and SVM. Also, this project will be especially useful in data preprocessing, hash vectorization, TF-IDF, etc. The only thing this library is not intended for is computing for large-scale production environment applications.
SciPy ends up on our list of top machine learning libraries in C++. In a nutshell, it is one of the best ready-made ML solutions for scientific and engineering goals but can also be used in commercial projects (because of its advanced support of natural language processing).
At the heart of this project lies a popular NumPy library for Python. Therefore, the main part of it is based on the work of NumPy modules (multidimensional arrays). However, the possibilities of SciPy also go beyond its prototype: for example, there are functions for the digitalization of tasks from linear algebra such as Fourier transform, image optimization, integration interpolation, differential equation solving, signal and image processing, and so on. And finally, it provides a bunch of useful packages, such as cluster, fft, interpolates, and ndimage.
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EPAM Anywhere Business’ Experience in Delivering Top-Notched Talents to Use C++ ML Libraries and Win Technical Resilience
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In the above list, obviously, we have not considered all the existing C++ machine learning libraries: in fact, there are many more of them. However, if you are looking for the most versatile and, at the same time, advanced ones, the first three points from our list (TensorFlow, Caffe, and Microsoft Cognitive Toolkit) will surely help you choose the right solution.
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