The global AI market is expected to grow from USD 387.45 billion in 2022 to USD 1,394.30 billion in 2029, increasing at a 20.1 per cent compound annual growth rate over the forecast period. The industry is likely to gain traction over the next several years, thanks to increased investment in AI technology by businesses of all kinds and across sectors.

AI is no longer a sci-fi buzzword. Each day, this new field of growing science moves closer to becoming a reality. Siri, Google Now, recommendation engines, and drones redefine how technology becomes more participatory and humanistic. According to Bloomberg's Jack Clark, 2015 was a watershed year for artificial intelligence, with the number of Google software projects incorporating AI increasing from "sporadic usage" in 2012 to more than 2,700.

AI Developers use machine learning to teach machines to learn from their own experiences without explicitly programming them. This article lists a variety of frameworks, tools (kits), modules, libraries, and other resources to help us accomplish so much with machine learning.

It's easy to fall back into popular platforms like TensorFlow and PyTorch, but many alternative open-source resources might aid your AI research. The truth is that there is a lot of exciting work and many fantastic new tools.

In this article, let us look at some of the top AI libraries available and their features in this article. 

OpenCV

Over 2,500 optimized algorithms for various computer vision use cases are available in the Open Source Computer Vision Library (OpenCV). OpenCV enables comprehending visual information as simple as calling the proper function and specifying the correct details, from detecting/recognizing faces to classifying human behaviours. Furthermore, OpenCV is ideal for adding computer vision infrastructure to a project because of its active community and abundant documentation.

Scikit-learn

Scikit-learn is a Python AI package that allows the development of machine learning algorithms easier utilizing the popular NumPy, SciPy, and matplotlib libraries. In addition, it features built-in algorithms for classifying items, building regressions, clustering related objects, reducing random variable quantity, preparing data, and even comparing/choosing your final model for you. 

TensorFlow

TensorFlow is an open-source software library for numerical computations using data flow graphs. This framework is well-known for its architecture, enabling computing on any CPU or GPU, whether on a desktop, a server, or even a mobile device. 

XGBoost

Extreme gradient boosting is abbreviated as XGBoost. This Python AI package focuses on boosting decision-tree algorithms to assist developers in categorizing data and generating regressions. The children of weaker regression models make up these trees (that represent different computation tasks). XGBoost considerably improves scalability and speed, making it ideal for keeping up with your program's growth.

NLTK

NLTK is a Python AI package with defined methods and interfaces that simplify rudimentary linguistics. NLTK is a general-purpose NLP library (or 'toolkit') that belongs to any language-based project, from tokenizing and tagging text to identifying named entities and showing parse trees.

spaCy

The developers of spaCy have dubbed it "the Ruby on Rails of Natural Language Processing." SpaCy makes processing big swaths of text quick and easy because of its relatively simple API. SpaCy can help your software grasp all parts of a text or pre-process it for one of the other AI libraries. They are providing and integrating tokenizer, tagger, parser, pre-trained word vectors, and named entity recognition facilities into one library.

FANN

FANN (Fast Artificial Neural Network Library) is a library that creates artificial neural networks in C (up to 150 times quicker than existing libraries) and makes them accessible in various languages, including Python. Moreover, it's exceedingly simple, requiring only three function calls to create, train, and run an artificial neural network. Thanks to its excellent documentation, comprehensive training framework, and parameter variety, it's a must-have for any project that uses neural networks.

Microsoft CNTK

The Computational Network ToolKit from Microsoft is a library that improves the modularization and maintenance of separating compute networks while also providing learning methods and model descriptions. Moreover, CNTK can take advantage of multiple servers at the same time.

Ffnet

A Python AI module, feed-forward neural networks, visualize training datasets via a graphical user interface. Another significant advantage is its automatic data normalization feature, which saves a considerable amount of time during the pre-processing stage of your workflow. Ffnet uses Fortran to implement its fundamental functions, resulting in significantly faster programs (compared to Python native solutions).

PyCLIPS

Python applications can use PyCLIPS as an inference engine. It includes a rules-based engine in the form of binary modules via classes and functions. In addition, the machine is kept "alive" in a memory space separate from the Python area, ensuring that inferences and rules as your program grows in complexity.

Caffe

Caffe is a deep learning network with trained neural networks already preloaded. If you have a tight deadline, this should be your first choice. In addition, this framework is well-known for its image processing capabilities, providing MATLAB support.

Theano

Theano framework facilitates deep learning research and is capable of offering accuracy for networks that require high processing capacity by using GPUs instead of CPUs. Multi-dimensional array processing, for example, necessitates a lot of power, which Theano can provide.

Torch

Torch is an open-source framework to do numerical calculations. In addition, the torch includes many algorithms for developing deep learning networks more quickly. Facebook's and Twitter's AI labs use the torch. PyTorch is a python-based framework that has shown to be easier to use and more dependable.

Accord.net

Accord.net is a C# framework for building neural networks for audio and picture processing. In addition, accord.net can create computer vision, signal processing, and statistics applications.

Spark MLib

Apache's Spark MLib framework is compatible with R, Scala, Java, and Python. In addition, it supports Hadoop workflows for machine learning techniques such as classification, regression, and clustering. It is also compatible with the cloud, Apache, and standalone systems, apart from Hadoop.