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  5. Aerosolve vs Torch

Aerosolve vs Torch

OverviewComparisonAlternatives

Overview

Aerosolve
Aerosolve
Stacks27
Followers73
Votes0
GitHub Stars4.8K
Forks565
Torch
Torch
Stacks355
Followers61
Votes0
GitHub Stars9.1K
Forks2.4K

Aerosolve vs Torch: 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 Torch? An open-source machine learning library and a script language based on the Lua programming language. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

Aerosolve and Torch 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, Torch provides the following key features:

  • A powerful N-dimensional array
  • Lots of routines for indexing, slicing, transposing
  • Amazing interface to C, via LuaJIT

Aerosolve is an open source tool with 4.58K GitHub stars and 578 GitHub forks. Here's a link to Aerosolve's open source repository on GitHub.

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CLI (Node.js)
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Detailed Comparison

Aerosolve
Aerosolve
Torch
Torch

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.

It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

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;Separate lightweight Java inference code;Scala code for training;Simple image content analysis code suitable for ordering or ranking images
A powerful N-dimensional array; Lots of routines for indexing, slicing, transposing; Amazing interface to C, via LuaJIT; Linear algebra routines; Neural network, and energy-based models; Numeric optimization routines; Fast and efficient GPU support; Embeddable, with ports to iOS and Android backends
Statistics
GitHub Stars
4.8K
GitHub Stars
9.1K
GitHub Forks
565
GitHub Forks
2.4K
Stacks
27
Stacks
355
Followers
73
Followers
61
Votes
0
Votes
0
Integrations
No integrations available
Python
Python
SQLFlow
SQLFlow
GraphPipe
GraphPipe
Flair
Flair
Pythia
Pythia
Databricks
Databricks
Comet.ml
Comet.ml

What are some alternatives to Aerosolve, Torch?

TensorFlow

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

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

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.

Keras

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

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