Top 8 open source AI technologies in machine learning
Artificial intelligence (AI) technologies are quickly transforming almost every sphere of our lives. From how we communicate to the means we use for transportation, we seem to be getting increasingly addicted to them.
Because of these rapid advancements, massive amounts of talent and resources are dedicated to accelerating the growth of the technologies.
Here is a list of 8 best open source AI technologies you can use to take your machine learning projects to the next level.
Initially released in 2015, TensorFlow is an open source machine learning framework that is easy to use and deploy across a variety of platforms. It is one of the most well-maintained and extensively used frameworks for machine learning.
Created by Google for supporting its research and production objectives, TensorFlow is now widely used by several companies, including Dropbox, eBay, Intel, Twitter, and Uber.
TensorFlow is available in Python, C++, Haskell, Java, Go, Rust, and most recently, JavaScript. You can also find third-party packages for other programming languages.
The framework allows you to develop neural networks (and even other computational models) using flowgraphs.
Initially released in 2015, Keras is an open source software library designed to simplify the creation of deep learning models. It is written in Python and can be deployed on top of other AI technologies such as TensorFlow, Microsoft Cognitive Toolkit (CNTK), and Theano.
Keras is known for its user-friendliness, modularity, and ease of extensibility. It is suitable if you need a machine learning library that allows for easy and fast prototyping, supports both convolutional and recurrent networks, and runs optimally on both CPUs (central processing units) and GPUs (graphics processing units).
Initially released in 2007, scikit-learn is an open source library developed for machine learning. This traditional framework is written in Python and features several machine learning models including classification, regression, clustering, and dimensionality reduction.
Scikit-learn is designed on three other open source projects—Matplotlib, NumPy, and SciPy—and it focuses on data mining and data analysis.
Initially released in 2016, the Microsoft Cognitive Toolkit (previously referred to as CNTK), is an AI solution that can empower you to take your machine learning projects to the next level.
Microsoft says that the open source framework is capable of “training deep learning algorithms to function like the human brain.”
Some of the vital features of the Microsoft Cognitive Toolkit include highly optimized components capable of handling data from Python, C++, or BrainScript, ability to provide efficient resource usage, ease of integration with Microsoft Azure, and interoperation with NumPy.
