Top 10 C ++ frameworks for machine learning in 2021
Machine learning is about calculations, and libraries help machine learning specialists and designers perform calculation tasks without repeating confusing lines of code. It helps coders perform calculations quickly. C ++ is ideal for dynamic load adjustment, versatile storage, and the growth of huge big data frameworks and libraries. With some of the unique advantages of C ++ as a programming language (including memory management, performance characteristics, and system programming), it is one of the most effective tools for rapidly developing computer science libraries. scalable data and big data. Here are the 10 best C ++ frameworks for machine learning.
TensorFlow is a famous deep learning library created by Google with its environment of devices, libraries and community resources for machine learning. This library has a comprehensive and adaptable environment of local devices, libraries, and assets that enables analysts and engineers to build and deliver ML-powered applications without any hassle. Whether you are a specialist or a hobbyist, TensorFlow is an end-to-end platform that allows you to easily build and deploy ML models.
The Convolutional Architecture for Rapid Feature Integration or Caffe is written in C ++ for a deep learning structure, was created by the Berkeley Vision and Learning Center. This library’s layouts incorporate expressive engineering, extensible code, speed, and a large local area that promotes dynamic advancement in exploration and industry settings.
Microsoft Cognitive Toolkit (CNTK)
Written in C ++, Microsoft Cognitive Toolkit is a bundle of deep learning tools brought together that describes neural networks as a progression of computational advancements through a coordinated graph. It performs stochastic tilt learning (SGD, backpropagation error) with programmed separation and parallelization on various GPUs and servers. CNTK enables customers to effortlessly recognize and join famous pattern types such as anticipatory DNNs, convolutional networks (CNN), and recurring networks (RNN / LSTM).
mlpack is a fast and flexible machine learning library written in C ++. The library aims to provide fast and extensible implementations of cutting-edge machine learning algorithms. It also provides simple command line programs, Python bindings, Julia bindings, and C ++ classes that can be integrated into larger scale machine learning solutions.
Shark is a fast, specialty, general-purpose open source (C / C ++) machine learning library for applications and exams, with help for direct and nonlinear advancement, portion-based learning calculations, neural organizations and various other machine learning strategies.
Armadillo is a direct polynomial mathematics (C / C ++) library with functionality similar to Matlab. The library is renowned for the rapid transformation of exploration code into authoring conditions, for design recognition, PC vision, signal management, bioinformatics, information, econometrics, among others.
This library (C / C ++) is used for efficient similarity search and dense vector clustering. It contains algorithms that search sets of vectors of any size, down to those that may not fit in RAM. It also supports the optional GPU provided via CUDA and an optional Python interface.
Written in C ++, Open Neural Networks (OpenNN) is a library of open source neural networks for advanced analysis. The library contains sophisticated algorithms and utilities to handle the following artificial intelligence solutions such as classification, regression, forecasting, among others. The main advantage of this library is its high performance.
The Fast Artificial Neural Network (FANN) is an open source neural network library written in the C language. The library implements multi-layered C-based artificial neural networks with support for both fully connected and weakly connected networks. It is easy to use, versatile, well documented and fast. Features include backpropagation training, scalable topology training, cross-platform, and can use both floating-point and fixed-point numbers.
XGBoost – A parallelized optimized general purpose gradient amplification library.
ThunderGBM – A fast library for GBDTs and random forests on GPUs.
LightGBM – Microsoft’s fast, distributed, high-performance gradient amplification framework (GBDT, GBRT, GBM or MART) based on decision tree algorithms, used for ranking, classification and many other tasks of machine learning.
CatBoost – Improvement of the general purpose gradient on the decision tree library with support for out-of-the-box categorical features. It is easy to install, contains a fast inference implementation, and supports CPU and GPU (even multi-GPU) computation.
Share this article