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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. AutoGluon vs Swift AI

AutoGluon vs Swift AI

OverviewComparisonAlternatives

Overview

Swift AI
Swift AI
Stacks14
Followers52
Votes0
AutoGluon
AutoGluon
Stacks8
Followers38
Votes0

Swift AI vs AutoGluon: What are the differences?

Swift AI: A.I. and machine learning library written in Swift. Swift AI is a high-performance AI and machine learning library written entirely in Swift. We currently support iOS and OS X, with support for more platforms coming soon!; AutoGluon: *AutoML Toolkit for Deep Learning *. It automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on image, text, and tabular data.

Swift AI and AutoGluon can be primarily classified as "Machine Learning" tools.

Swift AI and AutoGluon are both open source tools. Swift AI with 5.66K GitHub stars and 548 forks on GitHub appears to be more popular than AutoGluon with 1.72K GitHub stars and 193 GitHub forks.

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Detailed Comparison

Swift AI
Swift AI
AutoGluon
AutoGluon

Swift AI is a high-performance AI and machine learning library written entirely in Swift. We currently support iOS and OS X, with support for more platforms coming soon!

It automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on image, text, and tabular data.

Feed-Forward Neural Network; Fast Matrix Library
Quickly prototype deep learning solutions for your data with few lines of code; Leverage automatic hyperparameter tuning, model selection / architecture search, and data processing; Automatically utilize state-of-the-art deep learning techniques without expert knowledge; Easily improve existing bespoke models and data pipelines, or customize AutoGluon for your use-case
Statistics
Stacks
14
Stacks
8
Followers
52
Followers
38
Votes
0
Votes
0
Integrations
Swift
Swift
Python
Python
Linux
Linux

What are some alternatives to Swift AI, AutoGluon?

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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