Lobe

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Swift AI

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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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- No public GitHub repository available -

What is Lobe?

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.

What is Swift AI?

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!

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What tools integrate with Lobe?
What tools integrate with Swift AI?
What are some alternatives to Lobe and Swift AI?
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.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
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.
CUDA
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
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