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Gluon vs Keras: What are the differences?

What is Gluon? Deep Learning API from AWS and Microsoft. A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.

What is Keras? Deep Learning library for Theano and TensorFlow. Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/.

Gluon and Keras can be primarily classified as "Machine Learning" tools.

Some of the features offered by Gluon are:

  • Simple, Easy-to-Understand Code: Gluon offers a full set of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers.
  • Flexible, Imperative Structure: Gluon does not require the neural network model to be rigidly defined, but rather brings the training algorithm and model closer together to provide flexibility in the development process.
  • Dynamic Graphs: Gluon enables developers to define neural network models that are dynamic, meaning they can be built on the fly, with any structure, and using any of Python鈥檚 native control flow.

On the other hand, Keras provides the following key features:

  • neural networks API
  • Allows for easy and fast prototyping
  • Convolutional networks support

Keras is an open source tool with 42.1K GitHub stars and 16K GitHub forks. Here's a link to Keras's open source repository on GitHub.

- No public GitHub repository available -

What is Gluon?

A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.

What is Keras?

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
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        What are some alternatives to Gluon and Keras?
        TensorFlow
        TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
        Photon
        The fastest way to build beautiful Electron apps using simple HTML and CSS. Underneath it all is Electron. Originally built for GitHub's Atom text editor, Electron is the easiest way to build cross-platform desktop applications.
        PyTorch
        PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
        JavaFX
        It is a set of graphics and media packages that enables developers to design, create, test, debug, and deploy rich client applications that operate consistently across diverse platforms.
        scikit-learn
        scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
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        Decisions about Gluon and Keras
        Conor Myhrvold
        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber | 6 upvotes 434.4K views
        atUber TechnologiesUber Technologies
        PyTorch
        PyTorch
        Keras
        Keras
        TensorFlow
        TensorFlow

        Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

        At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

        TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

        Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

        https://eng.uber.com/horovod/

        (Direct GitHub repo: https://github.com/uber/horovod)

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