7 Best CPP Machine Learning Libraries 2024

Here are some famous C++ Machine Learning Libraries. Some C++ Machine Learning Libraries' are Image Recognition, Natural Language Processing, Speech Recognition, Machine Learning, and Robotics.

Cpp machine learning libraries are libraries of code written in the C++ programming language that provide a set of tools for creating and working with machine learning models. These libraries typically include algorithms for classification, regression, clustering, feature extraction, and other types of machine learning tasks.

Let us look at these libraries in detail below.

opencv

• Includes a wide variety of image processing and computer vision algorithms.
• Offers a wide range of machine learning algorithms to make development easier and faster.
• Offers a modular structure which makes it easier to add new features as they become available.

keras

• Stark contrast to other C++ machine learning libraries which require significant coding knowledge.
• Highly modular and extensible.
• Excellent support for convolutional neural networks.

scikit-learn

• Designed to be accessible to everyone, including novice programmers.
• Provides built-in tools for model evaluation, selection and tuning.
• Built-in support for parallel computing.

caffe

• Designed to scale to large datasets and large-scale deployments.
• Supports distributed training and provides data parallelism on multiple GPUs.
• Rich set of command-line tools for data pre-processing, model training and evaluation, and deployment.

CNTK

• Built-in model-parallelism feature, which allows for distributed training of large models.
• Optimized to take advantage of multi-core CPUs, GPUs and other hardware accelerators.
• Provides an API for Python, which allows developers to use Python to write custom operations and build models.

mlpack

• Offers a variety of command line tools for quick and efficient machine learning tasks.
• Designed to be extensible, allowing users to create custom machine learning algorithms.
• Does not require compiling for each algorithm.

shogun

• Wide range of preprocessing and post-processing tools to help with data analysis.
• Has an active community of developers, who are constantly updating and improving the library.