Here are the top 10 best C/C++ libraries for Machine Learning that you should know
Machine Learning is the latest buzzword floating around. It deserves to, as it is one of the most interesting subfields of Computer Science. So what does Machine Learning really mean? Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
Why devs use C++ for Machine Learning?
If you are aware of Machine Learning basics you must know that one of the crucial steps, in order to learn machine learning if you are an engineer, is to implement algorithms from scratch. In that case, I’d agree that there are languages with better ready-to-use libraries. But, if you are going to implement an algorithm from scratch and you are familiar with C++, why wouldn’t you use that language? The only obvious reason is that C++ is a much harder language to deal with than, say, Python.
However, again, if you are familiar with the intricacies of the language (memory allocation, pointers, references, templates, then I think it is actually a plus that you use C++ instead of a more “user-friendly” language.
Another reason for using C++ for Machine Learning is that, as you mention, C++ is more efficient than most other languages. And, important libraries such as TensorFlow and Torch are implemented in C++ under the hood. As a matter of fact, many companies implement their machine learning algorithms in C++. Sure, they will also use Python and R for experimentation and prototyping, but many of the production algorithms will end up in C++.
So let’s take a look at the top 10 best C/C++ libraries that you can use for your Machine Learning project.
Top 10 C/C++ Libraries for Machine Learning:
- Microsoft Cognitive Toolkit (CNTK)
- SHARK Library
- mlpack Library
TensorFlow is a popular open-source software library for machine learning. This library has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers and developers build and deploy ML-powered applications easily.
Know more about TensorFlow from here
The torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
Learn more about Torch from here
Microsoft Cognitive Toolkit (CNTK)
Written in C++, Microsoft Cognitive Toolkit is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK allows users to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs).
Learn more about Microsoft Cognitive Toolkit from here.
Convolutional Architecture for Fast Feature Embedding or Caffe is a deep learning framework written in C++. The features of this library include expressive architecture, extensible code, speed, and large community which fosters active development in research and industry deployments.
Learn more on Caffe from here
Shogun is an open-source machine learning library that offers a wide range of efficient and unified machine learning methods. The library is implemented in C++ and offers automatically generated, unified interfaces to Python, Octave, Java/Scala, Ruby, C#, R, Lua. Shogun provides an easy combination of multiple data representations, algorithm classes, and general-purpose tools for rapid prototyping of data pipelines.
Learn more about Shogun from here
Written in C++, Open Neural Networks (OpenNN) is an open-source neural networks library for advanced analytics. The library contains sophisticated algorithms and utilities to deal with the following artificial intelligence solutions such as classification, regression, forecasting, among others. The main advantage of this library is its high performance.
Learn more on OpenNN from here
Fast Artificial Neural Network (FANN) is an open-source neural network library written in C language. The library implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. It is easy to use, versatile, well documented, and fast. The features include backpropagation training, evolving topology training, cross-platform, and can use both floating-point and fixed-point numbers.
know more about FANN from here
Dynamic Neural Network Toolkit or DyNet is a neural network library written in C++ (with bindings in Python) and is designed to be efficient when running on either CPU or GPU. DyNet builds its computational graph on the fly, which makes variable-input and variable-output models simple to implement with high performance. The library is well-suited for techniques like natural language processing, graph structures, reinforcement learning, and other such.
here is everything about DyNet
Shark is a fast, modular, general open-source machine learning library written in C++ language. The library provides methods for linear and nonlinear optimization, kernel-based learning algorithms, neural networks, and various other machine learning techniques. It serves as a powerful toolbox for real-world applications as well as for research.
Know more on SHARK Library from here
mlpack is a fast, flexible machine learning library, written in C++. The library aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. It also provides simple command-line programs, Python bindings, Julia bindings, and C++ classes which can be integrated into larger-scale machine learning solutions.
learn more about mlpack Library from here
These are the Top 10 C/C++ libraries that you can use for Machine Learning. Do mention in the comment section if we have missed any. For more news on tech and cybersecurity stay tuned on Android Rookies by subscribing to our newsletter from here.