Aerosolve

22
61
+ 1
0
Xcessiv

0
7
+ 1
0
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Aerosolve vs Xcessiv: 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, Xcessiv is detailed as "Fully managed web application for automated machine learning". A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

Aerosolve and Xcessiv 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, Xcessiv provides the following key features:

  • Fully define your data source, cross-validation process, relevant metrics, and base learners with Python code
  • Any model following the Scikit-learn API can be used as a base learner
  • Task queue based architecture lets you take full advantage of multiple cores and embarrassingly parallel hyperparameter searches

Aerosolve and Xcessiv are both open source tools. It seems that Aerosolve with 4.58K GitHub stars and 578 forks on GitHub has more adoption than Xcessiv with 1.19K GitHub stars and 95 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 Xcessiv?

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
What companies use Aerosolve?
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    What tools integrate with Aerosolve?
    What tools integrate with Xcessiv?
      No integrations found
      What are some alternatives to Aerosolve and Xcessiv?
      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/
      scikit-learn
      scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
      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.
      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.
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