Top 21 Artificial Intelligence Frameworks and Libraries

We hear a lot about Artificial Intelligence and the innovations done in this field.

Every day, our innovators, developers and researchers try to provide us with new technology to make human work more compatible and easy.

Is AI good for us or not? Really, I can’t describe.

Some believe we can look forward to a great future, while others think it means we are on the path to being superseded by our robotic overlords.

But, it can’t stop us from innovating. Right?😎

And, being a developer, are you eager to know about artificial intelligence frameworks and libraries?

If Yes! Let’s dive into it.

Artificial Intelligence Frameworks and Libraries

Library/Framework	Type	Features	Programming Languages	License
TensorFlow	Library	a particular focus on training and inference of deep neural networks	Python, C++, CUDA	Apache License 2.0
Sci-kit-learn	Library	features various classification, regression and clustering algorithms including support vector machines	Python, Cython, C and C++	BSD
Caffe	Framework	a deep learning framework made with expression, speed, and modularity in mind	C++	BSD
Keras	Library	provides a Python interface for artificial neural networks	Python	MIT License
Microsoft Cognitive Toolkit	Framework	describes neural networks as a series of computational steps via a directed graph	C++	MIT License
PyTorch	Library	used for applications such as computer vision and natural language processing	Python; C++; CUDA	BSD
Torch	Library	provides a wide range of algorithms for deep learning, and uses the scripting language LuaJIT	Lua, LuaJIT, C, CUDA and C++	BSD
Microsoft CNTK	Framework	describes neural networks as a series of computational steps via a directed graph	C++	MIT License
Accord.NET	Framework	machine-learning framework combined with audio and image processing libraries	C#	LGPLv3 and partly GPLv3
Spark MLlib	Library	Spark MLlib provides various machine learning algorithms such as classification, regression, clustering, and collaborative filtering	Java, Scala, Python, and R	Apache 2.0
MLPack	Library	machine-learning software library for C++, built on top of the Armadillo library	C++	BSD
Apache Spark	Library	open-source unified analytics engine for large-scale data processing	Scala, Java, SQL, Python, R, C#, F#	Apache 2.0 license
OpenCV	Library	provides a real-time optimized Computer Vision library, tools, and hardware	C/C++	Apache license
OpenNN	Library	implements neural networks, the main area of deep learning research	C++	LGPL
DyNet	Library	the Dynamic Neural Network Toolkit	Python, C++	Apache License
Shogun	Library	offers numerous algorithms and data structures for machine learning problems.	C++	BSD 3, GNU GPLv3
Fast Artificial Neural Network	Library	for developing multilayer feedforward Artificial Neural Networks.	C	LGPL
Robot Operating System	Library	an open-source robotics middleware suite	Python, C++, Lisp	Apache 2.0 license
Flux	Framework	an open-source machine-learning software library and ecosystem	Julia	MIT
ML.NET	Framework	ML.NET supports sentiment analysis, price prediction, fraud detection, and more using custom models.	C#, C++	MIT License
AForge.NET	Framework	computer vision and artificial intelligence	C#, C++	LGPLv3 and partly GPLv3‎

Wrapping it Up!

I hope, you find it worthwhile.

All the technologies like driving cars, automated robots are just the beginning, over the next few years we’ll many new innovations.

Are you willing to innovate?

If yes! what will be your innovation?🤔