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  1. Stackups
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  5. Microsoft Cognitive Toolkit vs Swift AI

Microsoft Cognitive Toolkit vs Swift AI

OverviewComparisonAlternatives

Overview

Swift AI
Swift AI
Stacks14
Followers52
Votes0
Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit
Stacks18
Followers21
Votes0
GitHub Stars17.2K
Forks4.4K

Microsoft Cognitive Toolkit vs Swift AI: What are the differences?

Developers describe Microsoft Cognitive Toolkit as "An open-source toolkit for deep learning". It is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. On the other hand, Swift AI is detailed as "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!.

Microsoft Cognitive Toolkit and Swift AI can be categorized as "Machine Learning" tools.

Microsoft Cognitive Toolkit and Swift AI are both open source tools. It seems that Microsoft Cognitive Toolkit with 16.3K GitHub stars and 4.34K forks on GitHub has more adoption than Swift AI with 5.59K GitHub stars and 544 GitHub forks.

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Detailed Comparison

Swift AI
Swift AI
Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit

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 is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph.

Feed-Forward Neural Network; Fast Matrix Library
Speed & Scalability; Commercial-Grade Quality; Easy-to-use architecture
Statistics
GitHub Stars
-
GitHub Stars
17.2K
GitHub Forks
-
GitHub Forks
4.4K
Stacks
14
Stacks
18
Followers
52
Followers
21
Votes
0
Votes
0
Integrations
Swift
Swift
C++
C++
Python
Python

What are some alternatives to Swift AI, Microsoft Cognitive Toolkit?

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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