Deep Learning

Top 5 Deep Learning Frameworks in 2020

Deep Learning is a subset of Machine Learning. Deep learning has gained a lot of name because of the rise in demand of machine learning and Artificial Intelligence.

Frameworks are tools which makes the implementation of an algorithm or a logic quickly. Deep Learning framework is a library or a tool that allows us to build models on deep learning easily.

In this article we are going to list some of the top deep learning frameworks in 2020.

The listing is created by analyzing the popularity of the frameworks and the modules which are there in respective frameworks.

1. Tensorflow

Tensorflow is developed by Google. It is google’s open-source platform for creating Machine Learning and Deep Learning models. Tensorflow is written in Python, C++ and CUDA. Tensorflow can also run with javascript with Tensorflow.js library.

2. PyTorch

PyTorch is developed by Facebook. It is also free and open-source Deep Learning framework. It is based on the Torch library and was designed for research prototyping and production deployment.

PyTorch is also written in Python, C++, CUDA.

3. Keras

Keras is developed by Microsoft. It is also free and open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.

Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. 

4. Caffe

Caffe was originally developed by University of California, Berkeley and is free to use and open source deep learning framework.

Caffe is written in C++, with a Python interface.

5. Deeplearning4j

Deeplearning4j is a deep learning programming library written for Java and the Java virtual machine and a computing framework with wide support for deep learning algorithms.

Deeplearning4j is also open source framework under Apache licence. It is basically used for JVM.