7 Best Open Source AI Libraries

What is an Open-Source AI library?

 

An open-source AI library is an open-source platform for ML programming. An end-to-end accessible platform, an open-source AI library is a Machine Learning framework that offers techniques and technologies for software development and the creation of applications. An AI library is empowered by artificial intelligence and is available for commercial and non-commercial uses by the public. 

 

In order to improve upon the existing ML techniques to support the software development process, open-source AI libraries are developed, helping the process of application software development faster and easier. It simply negates the necessity for complex infrastructure. 

 

Rather, an AI library is empowered by Machine Learning algorithms and frameworks that let public users access it whenever and wherever they want. Artificial intelligence has induced intuitive power into machines that have led to the performance of human-like actions by computers and robots. 

 

A brainchild of AI, AI libraries caught attention in the 21st century while making people believe that ML language code and programming can be made easier and more efficient. 

 

Consisting of ML algorithms, both supervised and unsupervised, these libraries act as digital hubs of software development technology and are thus highly prevalent in recent times. 

 

In the years to come, Open Source AI libraries will let people leverage more advanced algorithms and high-end techniques for technological developments.   

 

(Read more - Top AI Algorithms)

 

 

Pros of Open Source AI Libraries 

 

1. Productive Platforms

 

Open-source AI libraries are extremely productive platforms that make them advantageous in numerous aspects. 

 

Beginning with the innovative ML tools and techniques that they offer to their users, AI libraries lead to innovation and production of high-end software and applications that make them suitable platforms for ML development and evolution. 

 

 

2. Cost-effective and Compatible Innovations

 

Intended for commercial and non-commercial use, AI libraries are cost-effective and compatible platforms for users to innovate and create. 

 

Had such platforms not been created, users would have to purchase ML techniques and tools and rent out technologies that are easily available with the help of AI libraries. Even though these libraries are licensed, they are open to all. 

 

 

3. Long-term Accessibility

 

Another advantage of AI libraries is that it has long-term accessibility. Unlike some technologies that can be accessed for a very short span of time, AI Libraries allow users to access ML algorithms and tools for a long time. 

 

This makes these libraries highly advantageous and suitable for software development. 

 

 

4. Advanced ML models 

 

Not only do these platforms involve basic machine learning models and tools, but they are empowered by advanced models that add to their pros. 

 

Moreover, one can leverage these tools for as long as they wish to and thus, their software development process gets improvised and rather easy. 

 

 

5. Cloud Computing Architecture

 

One of the biggest advantages of open-source machine learning libraries is that they have a cloud computing architecture. 

 

Unlike offline software that allows people to share work progress when they are physically near to each other, cloud networks let users share work progress within a span of seconds, even when they are far from each other. This makes such platforms highly advantageous and efficient. 

 

(Related blog: Top 12 NLP python libraries)

 

 

7 Best Open Source AI Libraries 

 

While there are endless open-source AI libraries, here is a list of the 7 Best Open Source AI Libraries in the contemporary digital domain. Let us get started right away! 

 

1. TensorFlow 

 

An ‘end-to-end’ open-source platform for ML, TensorFlow by Google is a flexible, efficient, and easy-to-use framework. With features like robust production, easy development, and impactful experimentation, this open-source AI library was launched in the year 2015. 

 

However, this library has only been released in the year 2021. A differentiable programming platform and numerical computation. 

 

A platform intended to fuel API purposes, Tensorflow is a successful AI library with advanced Google AI tools. Written in Python and C++, TensorFlow is not only meant for ML programming, but it also integrates Deep Learning technologies for the user to apply. 

 

It is a math library induced by data flow and is highly effective when it comes to classification, understanding, perception, discovery, creation, and prediction.

 

“TensorFlow AI Service Partners offer a range of consulting and software solutions to help you innovate faster, solve smarter, and scale bigger with TensorFlow.”TensorFlow by Google 

 

 

2. Microsoft CNTK

 

The Microsoft CNTK or Microsoft Cognitive Toolkit is another open-source AI library that was launched in the year 2016. 

 

An open toolkit intended for commercial purposes, this Microsoft deep learning library lets users integrate and implement various Deep Learning techniques and ML models as well. 

 

Efficient in problem-solving cases and software generation, this toolkit is considered to be one of the best among other AI libraries. 

 

“CNTK is also one of the first deep-learning toolkits to support the Open Neural Network Exchange ONNX format, an open-source shared model representation for framework interoperability and shared optimization.”Cognitive Toolkit by Microsoft 

 

3. Apple Core ML

 

Core ML by Apple is yet another open-source AI library that lets users apply ML models in their apps and software. 

 

An easy and efficient way to leverage techniques and algorithms without worrying about a complicated structure, Core ML allows users to generate powerful features with the help of one-liner codes and algorithms. 

 

By using Cloudkit, users can successfully engage ML models and get started with their software development today! A component of Core ML, Xcode assists users to secure their ML models and keep them safe while working on additional elements. 

 

Perhaps Core ML is surely one of the best as it also allows the addition of neural networks that are advanced and high-end in nature, making applications highly capable. 

 

 

4. Keras Python

 

Easy experimentation and heavy load work are the major highlights of the Keras Python. A Python AI library, Keras is programmed in the Python program language and allows deep learning techniques to be integrated. 

 

An ‘industry-strength performance capability is what defines this open-source AI library. As it is one of the most used AI libraries among all, it is certainly more powerful, efficient, and flexible for utilization. 

 

Launched in 2016, Keras has always been a promising AI library with advanced machine learning algorithms in python. While hosting a set of advanced tools, it promotes experimentation and can support large clusters of hardware for that matter.  

 

(Top read: Deep learning algorithms)

 

 

5. PyTorch

 

Written in Python and C++, PyTorch is one of the best open-source AI libraries that allows optimization of performance and gives way for scalable training of a software application. 

 

With a rich set of tools and models to support API, PyTorch is also used for development in applications of Natural Language Processing or NLP. 

 

Backed by a cloud computing architecture, this AI library does not let the network become an obstruction in development and lets software development accelerate smoothly. 

 

Some of the artificial intelligence tools and techniques of the PyTorch ecosystem are - pystiche, bayesian active learning, glow, and skorch. All these tools enable applications to be user-friendly and compatible in terms of performance. 

 

6. Caffe

 

Written in C++, Caffe was created by Yangqing Jia and developed by Berkeley AI Research. Launched in 2017, this open-source AI library supports API without complex coding language and instead, promotes innovative developments. 

 

Its innovative architecture allows high-speed operation and functioning that, in turn, enables users to develop software applications whenever and wherever they want. 

 

That said, Caffe is intended towards image segmentation and image classification with the help of various deep learning models and tools. 

 

As Deep Learning models have progressed in object classification problems, Caffe incorporates these models to support image classification, making it a high-speed platform. 

 

“Caffe can process over 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still.” Caffe 

 

(Must read - Machine Learning v/s Deep Learning) 

 

 

7. Theano

 

A Python-powered AI library, Theano is another one from the lot that has stood out among the rest. One of the best AI libraries, Theano enables users to perform symbolic differentiation and optimizations. 

 

It is fully capable of performing large neural tasks that make it efficient and a popular AI library in the field of Deep Learning. Empowered by NumPy, a package of high-end mathematical configurations, Theano is a much faster AI library than any other library. 

 

Termed as a ‘foundational library’, Theano is a revolutionary development in Deep Learning Python programming. Although it is generally prescribed for extensive scientific purposes owing to its large-scale computational capabilities, Theano can be used in classroom courses too.   

 

(Must read: Top NLP examples)

 

 

Future of AI Libraries 

 

The future of AI libraries can be perceived in light of the increasing popularity of AI. As artificial intelligence is getting more and more popular day by day, people are starting to rely on AI models for technological progress. 

 

From software development to day-to-day affairs, AI is simply entering people’s lives in a coercive yet soft manner. As AI libraries let users work with ML models and tools in order to add to technological advancement, there seems no reason as to why AI libraries will stay aloof in the coming years. 

 

All in all, AI is all set to take over the digital world and will soon expand itself in any and every way possible.