9 Best C++ Artificial Intelligence Libraries 2024

Here are some famous C++ Artificial Intelligence Libraries. C++ Artificial Intelligence Libraries use cases include Natural Language Processing, Machine Learning, Computer Vision, Robotics, and Autonomous Vehicles.

C++ Artificial Intelligence Libraries are collections of code that are designed to make it easier for developers to create applications that feature intelligent behavior. These libraries typically provide pre-built algorithms and data structures for solving a variety of AI-related problems, such as pathfinding, game playing, natural language processing, and machine learning.

Let us have a look at these libraries in detail below.

tensorflow

• Offers a robust library of built-in algorithms, such as linear regression, deep learning, and clustering.
• Automatic differentiation capabilities help to quickly develop and optimize models.
• Includes a distributed computing framework, allowing users to easily scale their models on multiple machines.

opencv

• Offers extensive algorithms for image processing, machine learning, computer vision, and machine learning.
• Offers a wide selection of image pre- and post-processing functions.
• Contains a comprehensive set of tutorials, examples, and functions.

caffe

• Caffe's modularity allows for an expansive range of applications.
• Caffe's expressive architecture makes it easy to develop new layers and architectures.
• Caffe's focus on user-friendliness makes it ideal for newcomers to deep learning.

CNTK

• Robust and Flexible Modeling Capability.
• High Performance and Scalability.
• Support for Microsoft Cognitive Toolkit.

dlib

• Extensive set of visualization tools for data exploration.
• Easy-to-use Python interface for rapid prototyping.
• Robust tools for data preprocessing, feature engineering, and feature selection.

mlpack

• Provides a unified interface for a variety of machine learning algorithms.
• Supports both supervised and unsupervised learning.
• Several pre-defined modules for constructing, training, and evaluating predictive models.

shogun

• Highly portable and supports multiple platforms, including Linux, Mac OS X, and Windows.
• Provides a unified interface for multiple languages, including Python, Octave, R, Java, and C++.
• Written in C++ and optimized for speed, which is one of its key advantages over other AI libraries.

torch

• Built-in autograd system that allows for efficient and accurate backpropagation of gradients for training neural networks.
• Allows for GPU-accelerated neural networks, allowing for faster training and execution times.
• Wide range of packages and extensions available to expand its capabilities.

mllib

• Includes implementations of popular machine learning algorithms.
• Includes various optimization techniques such as feature engineering and parameter tuning.
• Designed with scalability and performance in mind.