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Aerosolve vs MXNet: What are the differences?
Developers describe Aerosolve as "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. On the other hand, MXNet is detailed as "A flexible and efficient library for deep learning". A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
Aerosolve and MXNet 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, MXNet provides the following key features:
- Lightweight
- Portable
- Flexible distributed/Mobile deep learning
Aerosolve and MXNet are both open source tools. It seems that MXNet with 17.5K GitHub stars and 6.21K forks on GitHub has more adoption than Aerosolve with 4.58K GitHub stars and 578 GitHub forks.
Pros of Aerosolve
Pros of MXNet
- User friendly2