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Aerosolve
Aerosolve

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46
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0
Pythia
Pythia

0
4
+ 1
0
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Aerosolve vs Pythia: What are the differences?

What is 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.

What is Pythia? Framework for vision and language multimodal research. A modular framework for supercharging vision and language research built on top of PyTorch.

Aerosolve and Pythia 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, Pythia provides the following key features:

  • Model Zoo
  • Multi-Tasking
  • Datasets: Includes support for various datasets built-in including VQA, VizWiz, TextVQA and VisualDialog

Aerosolve and Pythia are both open source tools. It seems that Aerosolve with 4.58K GitHub stars and 578 forks on GitHub has more adoption than Pythia with 2.6K GitHub stars and 310 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 Pythia?

A modular framework for supercharging vision and language research built on top of PyTorch.
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              What are some alternatives to Aerosolve and Pythia?
              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.
              scikit-learn
              scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
              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.
              ML Kit
              ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
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