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Aerosolve

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Caffe

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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
  • Speed
  • Community

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.

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What is Aerosolve?

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.

What is Caffe?

It is a deep learning framework made with expression, speed, and modularity in mind.

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    What are some alternatives to Aerosolve and Caffe?
    TensorFlow
    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
    Keras
    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
    PyTorch
    PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
    scikit-learn
    scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
    CUDA
    A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
    See all alternatives