In 2023, machine learning (ML) continues to be a rapidly evolving field, and C/C++ remains crucial for performance-critical applications. Here are some of the top libraries for ML in C/C++ along with detailed explanations of each:-
➽ TensorFlow C++:-
TensorFlow is one of the most popular ML frameworks. TensorFlow is an open-source ML framework developed by Google. It offers a C++ API for high-performance computation. It allows seamless integration with existing C/C++ codebases and is well-suited for deploying ML models in production systems. The C++ API allows you to build, train, and deploy ML models efficiently. It supports both CPU and GPU acceleration for high-performance computing.
➽ PyTorch C++:-
PyTorch, known for its flexibility and dynamic computation graph, provides a C++ frontend. This library is excellent for research and development in C++ and benefits from PyTorch's rich ecosystem. The C++ front end provides seamless integration with C++ codebases. It's suitable for tasks like computer vision, natural language processing, and reinforcement learning.
➽ MLPack:-
MLPack is a fast, scalable, and modular C++ machine learning library. It covers a wide range of ML algorithms, including regression, clustering, and dimensionality reduction. It emphasizes ease of use and modularity.
➽ Dlib:-
Dlib is a general-purpose C++ library that includes machine learning tools. It's known for its high-quality implementations of various ML algorithms, including SVMs and deep learning. Dlib is known for its simplicity and versatility.
➽ Shogun:-
Shogun is a versatile C++ library for large-scale machine learning. It covers a broad spectrum of ML algorithms, including support vector machines, neural networks, and k-means clustering. Shogun is well-suited for large-scale data processing.
➽ OpenCV:-
While primarily known for computer vision, OpenCV has expanded to include ML functionality. It's suitable for applications that require both computer vision and machine learning capabilities. OpenCV is primarily an image processing and computer vision library. It also includes ML functions for tasks like object detection and image classification. OpenCV is widely used in robotics and autonomous systems.
➽ Caffe:-
Caffe is a deep learning framework with a C++ API, optimized for performance. It's known for its speed and efficiency, making it suitable for real-time inference. It's popular in computer vision and image recognition applications. Both CPU and GPU acceleration are supported by Caffe.
➽ XGBoost C++:-
XGBoost is a gradient-boosting library that offers a C++ API. It excels in tabular data and structured ML tasks, achieving state-of-the-art results and winning numerous Kaggle competitions. XGBoost is known for its speed and accuracy.
➽ LightGBM C++:-
LightGBM is another gradient-boosting library with a C++ API. It's known for its efficiency and is suitable for handling large datasets. LightGBM is commonly used in competitions and business analytics.
➽ Eigen:-
Eigen is a C++ template library for linear algebra, often used in ML for mathematical operations. ML libraries like MLPack and Dlib leverage Eigen for numerical computations.
➽ Turi Create C++:-
Turi Create is a machine learning framework from Apple with C++ bindings. It's user-friendly and great for quickly prototyping ML solutions. It simplifies the development of ML models and is beginner-friendly. It's suitable for tasks like image classification and recommendation systems.
➽ FastText:-
FastText, developed by Facebook, is a C++ library for text classification and word embeddings. It's efficient and widely used for natural language processing (NLP) tasks. FastText supports multi-label classification and text quantization.
➽ ONNX Runtime:-
ONNX Runtime provides a C++ API for executing models defined in the Open Neural Network Exchange (ONNX) format. It's valuable for model deployment and inference. It's platform-agnostic and enables easy deployment of ONNX models. ONNX Runtime is valuable for AI inferencing.
➽ CNTK:-
Microsoft Cognitive Toolkit (CNTK) offers a C++ API for deep learning. While less popular than TensorFlow and PyTorch, it's suitable for specific use cases. CNTK supports various neural network architectures.
➽ ArrayFire:-
ArrayFire is a high-performance GPU library for numerical computing, including ML tasks. It includes GPU-accelerated functions for ML tasks, making it ideal for fast computations. ArrayFire is compatible with multiple programming languages, including C++.
➽ Shark:-
Shark is an open-source C++ library for machine learning that emphasizes performance. It provides a range of algorithms and tools for both supervised and unsupervised learning tasks.
➽ Vowpal Wabbit (VW):-
VW is a fast, online machine-learning library designed for large-scale and real-time machine-learning tasks. It is written in C++ and is particularly useful for text classification and regression.
➽ TorchScript and LibTorch:-
These are part of the PyTorch ecosystem and provide C++ bindings for PyTorch, making it possible to develop and deploy machine learning models in C++.
➽ Tensors C++:-
Tensors is a C++ library that provides multi-dimensional arrays with support for various data types. It's useful for low-level tensor operations in ML applications. Tensors C++ is lightweight and easy to integrate.
➽ GTSAM:-
The Graphical Models Toolkit (GTSAM) is a C++ library for factor graphs and optimization in robotics and sensor fusion. It's commonly used for SLAM (Simultaneous Localization and Mapping) and sensor data fusion.
➽ Cub:-
Cub is a C++ library from NVIDIA that provides reusable software components for CUDA applications. It's helpful when implementing GPU-accelerated ML algorithms.
➽ CXXNET:-
CXXNET is a lightweight and efficient C++ library for deep learning. It's designed for speed and can be used in various deep-learning tasks.
In 2023, these libraries empower C/C++ developers to work on a wide range of machine learning applications, from traditional ML to deep learning and beyond. Choosing the right library depends on your specific project requirements, performance constraints, and familiarity with the ecosystem. Always consider the library's community support, documentation, and compatibility with your hardware and software stack when making a decision.