Aerosolve vs Gluon

Aerosolve vs Gluon: What are the differences?

Aerosolve: A machine learning package built for humans (created by Airbnb). This library is meant to be used with sparse, interpretable features such as those that commonly occur in search (search keywords, filters) or pricing (number of rooms, location, price). It is not as interpretable with problems with very dense non-human interpretable features such as raw pixels or audio samples; 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.

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

Some of the features offered by Aerosolve are:

• A thrift based feature representation that enables pairwise ranking loss and single context multiple item representation.
• A feature transform language gives the user a lot of control over the features
• Human friendly debuggable models

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

• 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.

Aerosolve is an open source tool with 4.58K GitHub stars and 578 GitHub forks. Here's a link to Aerosolve's open source repository on GitHub.