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PyTorch

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Skyl.ai

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6
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Skyl.ai vs PyTorch: What are the differences?

Skyl.ai: Manage your Complete Machine Learning Workflow. Build & deploy ML models faster on unstructured data. No specialized skills required Easy-to-use & scalable SaaS platform.; PyTorch: A deep learning framework that puts Python first. 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.

Skyl.ai and PyTorch can be categorized as "Machine Learning" tools.

PyTorch is an open source tool with 36.4K GitHub stars and 9.19K GitHub forks. Here's a link to PyTorch's open source repository on GitHub.

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Pros of PyTorch
Pros of Skyl.ai
  • 14
    Easy to use
  • 11
    Developer Friendly
  • 10
    Easy to debug
  • 7
    Sometimes faster than TensorFlow
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    Cons of PyTorch
    Cons of Skyl.ai
    • 3
      Lots of code
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      - No public GitHub repository available -

      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 Skyl.ai?

      Build & deploy ML models faster on unstructured data. No specialized skills required. Easy-to-use & scalable SaaS platform.

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

      What companies use PyTorch?
      What companies use Skyl.ai?
        No companies found
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        What tools integrate with PyTorch?
        What tools integrate with Skyl.ai?
          No integrations found

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          What are some alternatives to PyTorch and Skyl.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/
          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.
          MXNet
          A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
          Torch
          It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
          See all alternatives