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    Deep learning framework

    In today’s world many organizations are move on to machine learning and artificial intelligence to improve the business processes and stay ahead in competition. But some organizations are not able to implement machine learning or AI for their processes due to different reasons that is why deep learning framework come in. they are interfaces, libraries, or tools which are open source. People having little or no knowledge of machine learning or AI can easily use.

    Following are the Deep learning framework which are used:

    – TensorFlow

    – Keras

    – PyTorch

    – Dl4J

    – Caffe

    – Chainer


    – It developed by Google’s Brain team.

    – It support Languages like Python and R.

    – Easily to see data flow through neural network.

    – Easy to build and used for robust machine learning production


    – It is fastest- growing deep learning packages.

    – It support high level neural network, which is written in python.

    – It is user friendly, use simple APIs and show exact what error is.

    – It is suitable for advanced research.


    – It is open source deep learning framework which is developed by Facebook.

    – It is generally used in broad companies like Google, Facebook and twitter.

    – It uses C++ frontend as python interface.

    – Due to hybrid front-end, it provides flexibility and speed.

    – Computer vision and natural language processing is broadly used applications in deep learning.


    – It is a high-level library which is designed for building complex neural network structure.

    – Purpose of sonnet to develop and create objects like to specific part of neural network.

    – In Sonnet, models which created they have raw TF code and written in other high-level libraries.


    – It is distributed Deep learning library which is written for Java and JVM (Java virtual machine).

    – Its unique open-source numerical computing library is very powerful.


    – Caffe stands for Conventional Architecture for Fast Feature Embedding.

    – It is written in C++ with Python interface and mostly used for image detection and classification.

    – It support GPU and CPU which is based on acceleration computational kernel libraries, like NVIDIA, cuDNN, and intelMLK.


    – It is written purely in python on top of Numpy and CuPy.

    – In this ChainerMN library use multiple GPU’s and deliver Super-fast performance.


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