Most Used Artificial Intelligence Libraries
1. TENSORFLOW – GOOGLE
Open source library by Google that enables to run efficient deep learning algorithms on CPU and GPU.
2. TORCH – PYTORCH – FACEBOOK
It is a library mostly used in scientific research projects. Python implementation is available with PYTORCH.
3. SCIKIT-LEARN – OPEN SOURCE
It is one of the most used machine learning libraries. It contains many main algorithms ready-made.
4. SPARK – APACHE
Shared by the University of California as open source. It is a Cluster Computing library.
Theano is a Python library designed for deep learning. Using the tool, you can define and evaluate mathematical expressions, including multidimensional arrays. The GPU-optimized tool comes with features like integration with NumPy, dynamic C code generation, and symbolic differentiation.
Caffe is a popular deep learning tool designed for building applications. Ph.D. by Yangqing Jia At UC Berkeley the tool has a good Matlab/C++/Python interface. The tool allows you to quickly apply neural networks to the problem without writing text, code. Caffe partially supports multi-GPU training. The tool supports operating systems such as Ubuntu, Mac OS X and Windows.
7. Microsoft CNTK
Microsoft cognitive toolkit is one of the fastest deep learning frameworks with C#/C++/Python interface support. The open source framework comes with powerful C++ API and is faster and more accurate than TensorFlow. The tool also supports distributed learning with built-in data readers. It supports algorithms such as Feed Forward, CNN, RNN, LSTM, and Queue-to-Sequence. The tool supports Windows and Linux.
8. Azure ML Studio
Azure ML studio is a modern cloud platform for data scientists. It can be used to develop ML models in the cloud. With a wide variety of modeling options and algorithms, Azure is ideal for building larger ML models. The service provides 10GB of storage per account. It can be used with R and Python programs.
9. Amazon Machine Learning
Amazon Machine Learning (AML) is an ML service that provides tools and wizards for building ML models. With visual aids and easy-to-use analytics, AML aims to make ML more accessible to developers. AML can connect to data stored in Amazon S3, Redshift or RDS.
Written in Python, Keras is an open source library designed to facilitate the creation of new DL models. This high-level neural network API includes TensorFlow, Microsoft CNTK, etc. It can be run on top of deep learning frameworks such as Known for its user-friendliness and modularity, the tool is ideal for rapid prototypes. The tool is optimized for both CPU and GPU.