Need advice about which tool to choose?Ask the StackShare community!
Aerosolve vs Keras: 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, Keras is detailed as "Deep Learning library for Theano and TensorFlow". Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/.
Aerosolve and Keras 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, Keras provides the following key features:
- neural networks API
- Allows for easy and fast prototyping
- Convolutional networks support
Aerosolve and Keras are both open source tools. It seems that Keras with 42.5K GitHub stars and 16.2K forks on GitHub has more adoption than Aerosolve with 4.58K GitHub stars and 578 GitHub forks.
For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.
We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.
Pros of Aerosolve
Pros of Keras
- Quality Documentation8
- Supports Tensorflow and Theano backends7
- Easy and fast NN prototyping7
Sign up to add or upvote prosMake informed product decisions
Cons of Aerosolve
Cons of Keras
- Hard to debug4
