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Gluon: A Collaborative and Interactive Deep Learning Library

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Gluon: A Collaborative and Interactive Deep Learning Library

With Gluon, developers can quickly and easily build machine learning models — and while learning these models, they will not have to compromise on performance.

· AI Zone ·
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Microsoft and AWS recently unveiled a new joint project: an open-source deep learning library called Gluon. The Gluon API is good for developers with little machine learning experience but who know how to use Python. Moreover, the API also has some pre-built deep learning templates that can be used to run neural network models more quickly with less coding.

Machine learning models are helpful and crucial for any artificial intelligence system. Developers can run various services like scripts, messaging bots, face recognition, voice-based home hubs, or autonomous driving systems. These models also have to learn a lot through ingesting a vast quantity of data — and that is where Gluon can speed up the process.

This article discusses how to use this deep learning API and its features. With Gluon, developers can quickly and easily build machine learning models — and while learning these models, they will not have to compromise on performance.

Neural Network Models

A neural network model can take a week or more to develop and parse vast amounts of data. The complete development and training process is lengthy — it takes too much time to write code for the neural network, which is further hard to debug, reuse, and modify. Even experienced developers find it hard to build a new neural network. Gluon supports programming languages that are not supported by other frameworks.

Power of Gluon

The Gluon framework allows developers of all skill sets to build a prototype of, deploy, and train a cloud-based machine learning model for mobile apps. Other deep learning tools and frameworks may require writing lengthy code, but with Gluon, developers can do this with simple code. Other frameworks can also slow down the speed of training, which is not in the case with Gluon. 

Easy-to-Understand and Simple Lines of Code

With Gluon's plug-and-play building block set, it becomes easy to write code for any new network. Optimizers, layers, and initializers can be developed easily and conveniently.

Supplied Structure

In Gluon, the training algorithm and neural network are brought together, so greater flexibility exists in the API. Due to such flexibility, the code becomes easy to debug, and even more advanced models can be generated through Gluon.

Vigorous Graph Abilities

Using Gluon, developers can build any structure and use the native control flow of Python.

Soaring Performance

The performance of Gluon is really high, and you won’t have to sacrifice training speed due to Gluon.

How Is Gluon Different From Other Learning Models?

As per AWS and Microsoft, there are four attributes that set Gluon apart from other learning models:

  1. Gluon interface, which helps developers to write clear and concise code

  2. Gluon can be used to create easy-to-use debugging networks, making it quicker and easier to adopt.

  3. More sophisticated models and algorithms can be created through Gluon without compromising quality.

  4. Gluon provides all of these benefits without compromising training process speed; instead, it actually increases training speed.

AWS and Microsoft

AWS and Microsoft have collaborated this time to provide a better tool for AI developers. Alexa and Cortana are two voice-responsive digital assistants by these two companies that already improve the productivity of customers. Microsoft says:

“We believe that the industry must work together to build technology and to pool the resources to provide technology benefits to the broader community.”

Further Gluon Roadmap

The currently released Gluon version supports Apache MXNet and is available on GitHub. The future release of the tool will likely also support Microsoft Cognitive Toolkit and even more frameworks. The tool is helping developers leverage AI and speeding up the process of developing machine learning models.

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Topics:
aws ,microsoft ,ai ,machine learning ,gluon ,mxnet ,deep learning ,api ,ai applications

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