Swift AI vs TensorFlow

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Swift AI

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TensorFlow

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Swift AI vs TensorFlow: What are the differences?

What is 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!.

What is TensorFlow? Open Source Software Library for Machine Intelligence. 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.

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

Swift AI is an open source tool with 5.57K GitHub stars and 542 GitHub forks. Here's a link to Swift AI's open source repository on GitHub.

Decisions about Swift AI and TensorFlow

Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.

I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.

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Xi Huang
Developer at University of Toronto · | 8 upvotes · 53.8K views

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

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Pros of Swift AI
Pros of TensorFlow
    Be the first to leave a pro
    • 25
      High Performance
    • 16
      Connect Research and Production
    • 13
      Deep Flexibility
    • 9
      Auto-Differentiation
    • 9
      True Portability
    • 2
      Easy to use
    • 2
      High level abstraction
    • 1
      Powerful

    Sign up to add or upvote prosMake informed product decisions

    Cons of Swift AI
    Cons of TensorFlow
      Be the first to leave a con
      • 9
        Hard
      • 5
        Hard to debug
      • 1
        Documentation not very helpful

      Sign up to add or upvote consMake informed product decisions

      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!

      What is 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.

      Need advice about which tool to choose?Ask the StackShare community!

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        What are some alternatives to Swift AI and TensorFlow?
        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/
        scikit-learn
        scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
        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.
        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
        See all alternatives