Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include:
- Speech recognition
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.
These rapid development has sown a source for utilizing the massive amount of talent and resources dedicating to the growth of technologies.
A collection of best AI technologies are given,
1TENSOR FLOW :
TensorFlow was created by Google and was initially released in the year 2015. It 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.
2SCI-KIT LEARN :
The Sci-Kit learn was developed by David Cournapeau as a Google summer of code project. This is largely written in Python with some core algorithms written in Cython to achieve performance.
Scikit-learn is designed on three other open source projects—Matplotlib, NumPy, and SciPy—and it focuses on data mining and data analysis.
Keras is an open source neural network library written in Python released in the year of 2015. It 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 extension.
4MICROSOFT COGNITIVE TOOLKIT :
Microsoft Cognitive Toolkit previously known as CNTK was initially released in 2016, it is a deep learning framework developed by Microsoft research. It describes neural networks as a series of computational steps via a directed graph.
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.