numericaal vs PyTorch vs Swift AI


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Swift AI

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Advice on numericaal, PyTorch, and Swift AI
Adithya Shetty
Student at PES UNIVERSITY · | 5 upvotes · 39.3K views
Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

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          What is numericaal?

          numericaal automates model optimization and management so you can focus on data and training.

          What is 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.

          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 are some alternatives to numericaal, PyTorch, and Swift AI?
                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.
                Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano.
                scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
                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.
                ML Kit
                ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
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