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Being a compiled language, C++, the go-to framework for developers, can translate into machine code before execution, making it ideal for computationally intensive jobs like training large neural networks. Its robust memory management provides optimisation opportunities for machine learning algorithms.
Moreover, it seamlessly integrates with other tools such as CUDA and OpenCL, enabling GPU-based computing, which significantly enhances deep learning tasks’ speed. There are tons of such libraries available such as TensorFlow, Caffe, and mlpack. Let’s take a look at some of the C/C++ libraries for machine learning.
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OpenCV, short for Open Source Computer Vision, is a widely recognised C library known for its versatility and robustness. Supported by high-level programming languages such as Java and Python, it seamlessly integrates with deep learning frameworks like TensorFlow, Torch, and PyTorch. This open-source package boasts a comprehensive collection of over 2,500 optimised algorithms, catering to various functions such as face recognition, object detection, 3D model extraction, and more.
Originally initiated by Intel in 1999, it has gained cross-platform compatibility, with interfaces for C++, Python, Java, and MATLAB, and supports Windows, Linux, Android, and Mac OS. Despite its C foundation, OpenCV remains a go-to choice for machine learning and computer vision applications, making it a popular tool with over 29,000 weekly downloads. MobileNets + Single Shot Detectors and YoloV5 are based on OpenCV.
Facebook AI Similarity Search (FAISS) is a powerful library for swiftly discovering similar multimedia documents, excelling with datasets of billions of examples and offering an impressive 8.5 times speed boost compared to the fastest k-sampling GPU algorithm. Developed by Facebook AI Research, Faiss is a C++ tool optimized for efficient similarity search and clustering of dense vectors. It delivers diverse similarity search methods, is highly parallelizable for multi-core and multi-GPU utilisation, and comes with Python wrappers.
Faiss is open source, residing on GitHub, and includes code for evaluation and parameter tuning. It accommodates datasets of any size, even those exceeding RAM capacity, drawing from extensive research. Notably, Faiss outpaces Scikit-Learn by 20 times in k-means clustering speed. However, it’s essential to consider its suitability for specific machine-learning tasks.
Scikit-Learn is a robust Python library built on NumPy and SciPy, serving as a valuable tool for data preprocessing and modelling tasks encompassing supervised and unsupervised learning. With an active and supportive community, it offers solutions to various queries. Its versatile applications include decision trees, linear and logistic regression, classification, clustering, and SVM, particularly in data preprocessing, hash vectorization, and TF-IDF. However, it’s not designed for large-scale production environments. Random Forest Classifiers, Support Vector Machines (SVM), K-Means Clustering, and Gradient Boosting are based on this.
A scientific computing library for Python, SciPy offers modules for optimization, linear algebra, integration, interpolation, signal and image processing, and more, with C and C++ interfaces. While it’s not explicitly tailored for machine learning, it can still be applied in tasks like data preprocessing and feature extraction. It stands as one of the top machine learning libraries in C++, serving scientific, engineering, and commercial projects due to its robust support for natural language processing. SciPy builds upon the popular NumPy library, extending its capabilities to encompass tasks like Fourier transforms, image optimization, integration, interpolation, differential equation solving, and more, offering valuable packages like cluster, fft, interpolates, and ndimage.
Armadillo is a C++ library known for its proficiency in linear algebra and scientific computations, boasting strong performance, seamless integration with C++ code, a user-friendly API, and open-source accessibility. While it isn’t a full-fledged machine learning library, it excels at efficient linear algebra tasks, making it an attractive choice for C++ developers requiring swift mathematical computations. For comprehensive machine learning endeavours, it’s common to combine Armadillo with specialised libraries like TensorFlow, PyTorch, or Scikit-Learn to handle broader machine learning functionalities.
In addition to linear algebra, Armadillo provides a rich toolkit for managing vectors, matrices, and cubes, featuring a robust expression parser, adaptive matrix evaluation, diverse matrix decomposition techniques, OpenMP multi-threading support, and more. These capabilities collectively position Armadillo as an excellent option for developing applications across various domains, including machine learning, pattern recognition, computer vision (with a particular emphasis on medical applications), signal processing, statistics, and beyond.