This article briefly describes the best open source tools for deep learning, as well as some of the most interesting research into the subject. A number of open-source deep learning tools are used in this field. We take a look at some of the best examples of how machine learning can help us interact with the world around us.
Top 5 open source deep learning tools:
TensorFlow (www.tensorflow.org) is an open-source deep learning framework for the development of machine learning models. TensorFlow also provides extensive support for multiple computer graphics processing units (GPUs).
Keras (keras.io) is an open-source deep-learning library in Python, released on March 27, 2015.
Extensive documentation and developer guides.
Easy to learn and use.
Supports models for mobile devices, the Web as well as Java Virtual Machine.
PyTorch (pytorch.org) is an open-source machine learning library. It was released in September 2016 and the essential elements (cheat sheet) for new users are available. Offers multiple GPU support for the implementation of deep learning models. Supports C++ interface for high performance and low latency applications.
OpenNN (www.opennn.net) is an open-source neural networks library for machine learning and deep learning. Its first version (0.1) was released on November 22, 2018. It allows users to create a neural network model without programming. Outstanding performance in terms of execution speed and memory allocation.
5. Microsoft Cognitive Toolkit (CNTK):
Microsoft Cognitive Toolkit (CNTK) is a commercial-grade open-source deep learning framework. CNTK’s primary release was on January 25, 2016, and its APIs are available in Python, C#, C++, and Java. It provides an easy and simple way to combine various deep learning models like a convolutional neural network.