Aerosolve vs Caffe: 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; Caffe: A deep learning framework. It is a deep learning framework made with expression, speed, and modularity in mind.
Aerosolve and Caffe can be categorized 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, Caffe provides the following key features:
- Extensible code
Aerosolve and Caffe are both open source tools. Caffe with 29.2K GitHub stars and 17.6K forks on GitHub appears to be more popular than Aerosolve with 4.58K GitHub stars and 581 GitHub forks.