Top 23 AI Open Source Software Libraries

Sign up for Free Trial

What are Open Source Software Libraries?

Open-source software libraries are collections of pre-written code that have been made publicly available for anyone to use, modify, and distribute. These libraries contain reusable code that can be integrated into software projects to save time and effort. They are typically maintained and updated by a community of developers, who contribute their expertise and experience to improve the functionality and usability of the library. Users can submit bug reports, feature requests, and code contributions to help improve the library for everyone.

The nature of these libraries means that they are freely available for use and distribution, which can significantly reduce the cost of software development. Additionally, open-source software libraries can provide transparency and security since the code is available for review by anyone. Using these libraries can also help to promote collaboration and innovation since developers can build upon each other's work to create new and improved software applications. They are essential to the software development ecosystem, providing a valuable resource for developers worldwide.

History of Open Source Software Libraries:

Open-source software libraries have a rich history since the early days of computing. Here are some key milestones in the computing history associated:

1. The Free Software Movement: In the 1980s, the Free Software Foundation was founded by Richard Stallman, who believed that software should be free and open to everyone. This led to the development of the GNU Project, which aimed to create a completely free and open operating system.
2. The World Wide Web: The development of the World Wide Web in the early 1990s created new opportunities for sharing and distributing software. Many early web servers, such as the NCSA HTTPd server, were open source.
3. The Linux Operating System: In 1991, Linus Torvalds created the Linux operating system, which was released under an open-source license. Linux quickly became popular among developers and has since become one of the most widely used operating systems in the world.
4. The Apache Web Server: The Apache web server was created in 1995 and quickly became one of the most popular web servers in the world. Apache is open-source software, and its success helped to popularize the idea of open-source software in general.
5. The Open Source Initiative: In 1998, the Open Source Initiative (OSI) was founded to promote and advocate for the use of open-source software. The OSI developed the Open Source Definition, which provides guidelines for what qualifies as open-source software.
6. GitHub: In 2008, GitHub was founded as a platform for hosting and collaborating on open-source software projects. GitHub has since become one of the most popular platforms for open-source development, hosting millions of repositories and supporting millions of developers.
7. Modern Open Source Libraries: Today, there are thousands of open source libraries available for developers to use in their projects. Many popular programming languages, such as Python and JavaScript, have large ecosystems of open-source libraries that provide developers with powerful tools for building software.

The history of open software libraries is closely tied to the broader history of open-source software. As more and more developers have embraced the idea of open source, the availability and quality of open-source libraries have grown tremendously, making it easier than ever for developers to build powerful and flexible software applications.

Here are the Top 23 AI Open Source Software Libraries:

• TensorFlow: Many years ago, deep learning started to exceed all other machine learning algorithms when giving extensive data. Google has seen it could use these deep neural networks to upgrade its services: Google search engine, Gmail & Photo. They build a framework called TensorFlow to permit researchers and developers to work together in an AI model. Once it is approved and scaled, it allows lots of people to use it. It was first released in 2015, while the first stable version was coming in 2017. It is an open-source platform under Apache Open Source License. We can use it, modify it, and reorganize the revised version for free without paying anything to Google.

Github source code: https://github.com/tensorflow

• PyTorch: PyTorch is an open-source machine learning library used for developing and training neural network-based deep learning models. It is primarily developed by Facebook’s AI research group. PyTorch can be used with Python as well as C++. Naturally, the Python interface is glistening.

Github source code: https://github.com/pytorch/pytorch

• Theano: Theano is a Python library that allows you to define, optimize and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It was developed primarily by the Montreal Institute for Learning Algorithms (MILA) at the University of Montreal, and it was released in 2007. Theano provides a high-level interface for defining mathematical expressions, which are then optimized and compiled to run efficiently on both CPU and GPU architectures. This optimization process makes it possible to perform numerical computations many times faster than with pure Python code. Theano is also highly configurable, allowing users to customize its behavior to their specific needs.

Github source code: https://github.com/Theano/

• Microsoft Cognitive Toolkit: The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows users to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs), and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.

Github source code: https://github.com/microsoft/CNTK

• Torch: Torch is an open-source machine learning library for Python, based on the Lua programming language. It was originally developed by researchers at the Facebook AI Research Lab (FAIR) and has since been maintained and expanded by a community of developers. Torch provides a set of tools for building and training neural networks, including modules for building models, optimization algorithms, and data loaders. It also includes a scripting language, LuaJIT, which allows users to write scripts that can be executed efficiently on both CPUs and GPUs. Torch has been used for a wide range of applications, including natural language processing, computer vision, and speech recognition. In recent years, Torch has been largely superseded by PyTorch, a Python-based machine-learning library that was also developed by FAIR.

Github source code: https://github.com/pytorch/pytorch

• OpenCV: OpenCV is the huge open-source library for computer vision, machine learning, and image processing and now it plays a major role in real-time operation which is very important in today’s systems. Using it, one can process images and videos to identify objects, faces, or even the handwriting of a human. When it is integrated with various libraries, such as NumPy, python is capable of processing the OpenCV array structure for analysis. To Identify image patterns and their various features we use vector space and perform mathematical operations on these features. 

Github source code: https://github.com/opencv

• scikit-Learn: scikit-Learn is an open-source data analysis library, and the gold standard for Machine Learning (ML) in the Python ecosystem. Key concepts and features include: Algorithmic decision-making methods, including Classification: identifying and categorizing data based on patterns. 

Github source code: https://github.com/scikit-learn

• OpenNN: OpenNN is a software library written in C++ for advanced analytics. It implements neural networks, the most successful machine learning method. The main advantage of OpenNN is its high performance. This library stands out in terms of execution speed and memory allocation. It is constantly optimized and parallelized in order to maximize its efficiency. Some typical applications of OpenNN are business intelligence (customer segmentation, churn prevention...), health care (early diagnosis, microarray analysis,...), and engineering (performance optimization, predictive maintenance...).

Github source code: https://github.com/Artelnics/opennn

• mlpack: mlpack is intended for academic and commercial use, for instance by data scientists who need efficiency and ease of deployment, or, e.g., by researchers who need flexibility and extensibility. High-quality documentation is a development goal of mlpack.

Github source code: https://github.com/mlpack/mlpack

• Chainer: Chainer is a Python-based deep learning framework that was developed by Preferred Networks, Inc. It allows developers to create and train neural networks for a wide range of machine learning tasks, such as image recognition, natural language processing, and speech recognition. One of the key features of Chainer is its dynamic computational graph, which allows for the flexible and efficient execution of neural networks. This means that the graph structure of the network can be changed on-the-fly during training, which enables simplification of complicated models and efficient memory usage.

Github source code: https://github.com/chainer/chainer

• Dlib: Dlib is an open-source C++ library that is primarily used for machine learning and computer vision tasks. It is designed to provide efficient implementations of common algorithms and data structures for tasks such as object detection, face recognition, and image segmentation. One of the key features of Dlib is its ability to work with a wide range of data types, including images, audio, and text. This makes it a versatile tool for machine-learning applications. Dlib also includes a number of pre-trained models for common tasks, such as object detection using the Histogram of Oriented Gradients (HOG) feature descriptor, which can be easily integrated into a user's application.

Github source code: https://github.com/davisking/dlib

• Flux: Flux is an architecture pattern for building reactive applications. It is typically used in web applications to manage the flow of data between the user interface and the server-side logic. Flux was created by Facebook to address some of the challenges they faced when building complex web applications with a lot of interactivity. In a Flux architecture, data flows in one direction only, from the server-side logic to the user interface. This makes it easier to manage the state of the application and to keep track of changes in the data. Flux is made up of four main components: the dispatcher, the stores, the views, and the actions.

Github source code: https://github.com/fluxcd/flux2

• DyNet: DyNet is an open-source neural network toolkit that is designed to facilitate the development of dynamic neural networks, which are neural networks that can be constructed on-the-fly during runtime. It was developed by Carnegie Mellon University, and it supports both Python and C++. DyNet is particularly useful for developing models with complex, changing structures, such as those used in natural language processing tasks, where the input sequence length varies. With DyNet, users can construct computation graphs dynamically, allowing them to change the structure of the network as needed during runtime.

Github source code: https://github.com/clab/dynet

• CMU Sphinx: CMU Sphinx is a suite of open-source speech recognition tools developed by Carnegie Mellon University. It includes several components, such as acoustic models, language models, and decoders, which allow users to build speech recognition systems for various applications. The software is available under a permissive open-source license, which allows anyone to use, modify, and distribute it freely. CMU Sphinx supports a wide range of languages and dialects, making it a popular choice for researchers and developers working in multilingual environments. It can be used to build speech recognition systems for applications such as dictation, voice search, and voice control.

Github source code: https://github.com/cmusphinx

• fastText: fastText is an open-source, free, lightweight, and scalable library for text representation and classification developed by Facebook's AI Research (FAIR) team. It uses a combination of techniques from deep learning and traditional natural language processing (NLP) to efficiently represent and classify text. The core idea behind fastText is to represent words as vectors, which allows the library to capture both semantic and syntactic information. Additionally, fastText uses subword information, such as character n-grams, to handle out-of-vocabulary words and improve the accuracy of text classification tasks.

Github source code: https://github.com/topics/fasttext

• Shogun: Shogun is an open-source machine-learning software library that provides a wide range of algorithms for data analysis, machine learning, and artificial intelligence. It was initially developed at the Technical University of Berlin and is now maintained by a global community of contributors. Shogun offers a unified interface for various machine-learning tasks, including regression, classification, clustering, and dimensionality reduction. It supports a range of programming languages, including C++, Python, R, and Octave. One of the unique features of Shogun is its support for kernel machines, which allows users to easily build complex models and perform advanced data analysis. It also includes a number of other machine learning algorithms, such as support vector machines, decision trees, neural networks, and deep learning.

Github source code: https://github.com/shogun-toolbox/shogun

• Fast Artificial Neural Network (FANN): FANN stands for Fast Artificial Neural Network. It is an open-source software library written in C, designed to support the implementation of artificial neural networks (ANNs) for machine learning applications. FANN provides a simple interface for creating, training, and using ANNs, making it easy to implement machine learning algorithms in a variety of applications. The library supports a range of activation functions and training algorithms, allowing users to customize their ANNs to suit their specific needs. One of the key features of FANN is its speed. It is optimized for performance and can be used for both small and large-scale applications. It also supports parallel processing, making it ideal for use on multi-core systems.

Github source code: https://github.com/libfann/fann

• Acumos AI: Acumos AI is a platform and open-source framework that makes it easy to build, share, and deploy AI apps. Acumos standardizes the infrastructure stack and components required to run an out-of-the-box general AI environment. This frees data scientists and model trainers to focus on their core competencies and accelerates innovation.

Github source code: https://github.com/acumos

• ClearML: The ClearML Python Package for integrating ClearML into your existing scripts by adding just two lines of code, and optionally extending your experiments and other workflows with ClearML's powerful and versatile set of classes and methods. The ClearML Server stores experiment, model, and workflow data, and supports the Web UI experiment manager, and ML-Ops automation for reproducibility and tuning. It is available as a hosted service and open source for you to deploy your own ClearML Server. The ClearML Agent for ML-Ops orchestration, experiment and workflow reproducibility, and scalability.

Github source code: https://github.com/allegroai/clearml

• H20.ai: H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 18,000 organizations globally and is popular in both the R & Python communities.

Github source code: https://github.com/h2oai

• Mycroft.ai: Mycroft.ai is an open-source voice assistant software that can run on a variety of platforms, including Linux-based operating systems, Raspberry Pi, and even Windows. It was founded in 2015 and is developed by Mycroft AI, Inc., a company headquartered in Kansas City, USA. Mycroft.ai is designed to be a customizable and privacy-focused alternative to other popular voice assistants such as Amazon Alexa and Google Assistant. Users can program Mycroft.ai to perform a wide range of tasks using natural language commands, and the software can also integrate with smart home devices and other third-party services.

Github source code: https://github.com/MycroftAI

• Rasa OpenSource: With over 25 million downloads, Rasa Open Source is a popular open-source framework for building chat and voice-based AI assistants. Rasa Pro is an open-core product powered by an open-source conversational AI framework with additional analytics, security, and observability capabilities. Rasa Pro is a part of our enterprise solution, Rasa Platform. Another product that makes up Rasa Platform is Rasa X/Enterprise. It is our low-code user interface that supports conversational AI Teams reviewing and improving AI Assistants at scale. It must be used with Rasa Pro. 

Github source code: https://github.com/RasaHQ/rasa

• Tesseract OCR: Tesseract 4 adds a new neural net (LSTM) based OCR engine which is focused on line recognition, but also supports the legacy Tesseract OCR engine of Tesseract 3 which works by recognizing character patterns. Compatibility with Tesseract 3 is enabled by using the Legacy OCR Engine model (--oem 0). It also needs trained data files that support the legacy engine, for example, those from the tessdata repository.

Github source code: https://github.com/tesseract-ocr/tesseract

Before starting to build a machine learning application, selecting one technology from the many options out there can be a difficult task. Therefore, it's important to evaluate several options before making a final decision. Furthermore, learning how the various machine learning technologies work can assist you to make a good decision. Apart from the above-listed AI technologies in machine learning, which are you using in your projects? Is there any other framework, library, or toolkit not discussed?