Swift AI vs PySyft | What are the differences? | StackShare

PySyft vs Swift AI

Overview

Swift AI

Stacks14

Followers52

Votes0

PySyft

Stacks7

Followers24

Votes0

GitHub Stars9.8K

Forks2.0K

Swift AI vs PySyft: 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!; PySyft: A library for encrypted, privacy preserving machine learning. It is a Python library for secure and private Deep Learning. PySyft decouples private data from model training, using Federated Learning, Differential Privacy, and Multi-Party Computation (MPC) within the main Deep Learning frameworks like PyTorch and TensorFlow.

Swift AI and PySyft belong to "Machine Learning Tools" category of the tech stack.

Swift AI is an open source tool with 5.66K GitHub stars and 547 GitHub forks. Here's a link to Swift AI's open source repository on GitHub.

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Detailed Comparison

Swift AI	PySyft
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 a Python library for secure and private Deep Learning. PySyft decouples private data from model training, using Federated Learning, Differential Privacy, and Multi-Party Computation (MPC) within the main Deep Learning frameworks like PyTorch and TensorFlow.
Feed-Forward Neural Network; Fast Matrix Library	Secure and private Deep Learning; Decouples private data from model training
Statistics
GitHub Stars -	GitHub Stars 9.8K
GitHub Forks -	GitHub Forks 2.0K
Stacks 14	Stacks 7
Followers 52	Followers 24
Votes 0	Votes 0
Integrations
Swift	PyTorch Python TensorFlow

What are some alternatives to Swift AI, PySyft?

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.

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

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

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

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

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

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

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.