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

Lobe vs Swift AI

OverviewComparisonAlternatives

Overview

Swift AI
Swift AI
Stacks14
Followers52
Votes0
Lobe
Lobe
Stacks1
Followers18
Votes0

Lobe vs Swift AI: What are the differences?

Lobe: Deep learning made simple. An easy-to-use visual tool that lets you build custom deep learning models, quickly train them, and ship them directly in your app without writing any code; 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!.

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

Swift AI is an open source tool with 5.58K GitHub stars and 542 GitHub forks. Here's a link to Swift AI's open source repository on GitHub.

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Detailed Comparison

Swift AI
Swift AI
Lobe
Lobe

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!

An easy-to-use visual tool that lets you build custom deep learning models, quickly train them, and ship them directly in your app without writing any code.

Feed-Forward Neural Network; Fast Matrix Library
Build - Drag in your training data and Lobe automatically builds you a custom deep learning model. Then refine your model by adjusting settings and connecting pre-trained building blocks.; Train - Monitor training progress in real-time with interactive charts and test results that update live as your model improves. Cloud training lets you get results quickly, without slowing down your computer.; Ship - Export your trained model to TensorFlow or CoreML and run it directly in your app on iOS and Android. Or use the easy-to-use Lobe Developer API and run your model remotely over the air.
Statistics
Stacks
14
Stacks
1
Followers
52
Followers
18
Votes
0
Votes
0
Integrations
Swift
Swift
TensorFlow
TensorFlow
Keras
Keras

What are some alternatives to Swift AI, Lobe?

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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