Gluon vs ScalaNLP: What are the differences?
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; ScalaNLP: A suite of machine learning and numerical computing libraries. ScalaNLP is a suite of machine learning and numerical computing libraries.
Gluon and ScalaNLP 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’s native control flow.
On the other hand, ScalaNLP provides the following key features:
- ScalaNLP is the umbrella project for several libraries:
- Breeze is a set of libraries for machine learning and numerical computing
- Epic is a high-performance statistical parser and structured prediction library
ScalaNLP is an open source tool with 2.91K GitHub stars and 674 GitHub forks. Here's a link to ScalaNLP's open source repository on GitHub.