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Caffe

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Swift AI

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Swift AI vs Caffe: What are the differences?

Developers describe Swift AI 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!. On the other hand, Caffe is detailed as "A deep learning framework". It is a deep learning framework made with expression, speed, and modularity in mind.

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

Swift AI and Caffe are both open source tools. Caffe with 29.2K GitHub stars and 17.6K forks on GitHub appears to be more popular than Swift AI with 5.62K GitHub stars and 547 GitHub forks.

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What is Caffe?

It is a deep learning framework made with expression, speed, and modularity in mind.

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 Caffe?
    What tools integrate with Swift AI?

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    What are some alternatives to Caffe 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.
    Torch
    It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
    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.
    Caffe2
    Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.
    Keras
    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
    See all alternatives