For your need, you can use these AI technologies in machine learning:
1. TensorFlow
Initially released in 2015, TensorFlow is an open source machine learning framework that is easy to use and deploy across a variety of platforms. TensorFlow is available in Python, C++, Haskell, Java, Go, Rust, and most recently, JavaScript. The framework allows you to develop neural networks using flowgraphs.

2. Keras
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, and Theano.

3. Programming and development
This traditional framework is written in Python and features several machine learning models including classification, regression, clustering, and dimensionality reduction.

4. Microsoft Cognitive Toolkit
Microsoft says that the open source framework is capable of «training deep learning algorithms to function like the human brain».

5. Caffe
Initially released in 2017, Caffe is a machine learning framework that focuses on expressiveness, speed, and modularity. The open source framework is written in C++ and comes with a Python interface.

6. Torch
Initially released in 2002, Torch is a machine learning library that offers a wide array of algorithms for deep learning. Some of Torch's key features include N-dimensional arrays, linear algebra routines, numeric optimization routines, efficient GPU support, and support for iOS and Android platforms. Initially released in 2010, Accord. NET is a machine learning framework entirely written in C#.
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