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scikit-learn vs Gym: What are the differences?

Developers describe scikit-learn as "Easy-to-use and general-purpose machine learning in Python". scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license. On the other hand, Gym is detailed as "Open source interface to reinforcement learning tasks". It is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Pinball.

scikit-learn and Gym can be categorized as "Machine Learning" tools.

scikit-learn is an open source tool with 41.4K GitHub stars and 20K GitHub forks. Here's a link to scikit-learn's open source repository on GitHub.

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Pros of Gym
Pros of scikit-learn
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      Scientific computing
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      Easy

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    Cons of Gym
    Cons of scikit-learn
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        Limited

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      What is Gym?

      It is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Pinball.

      What is scikit-learn?

      scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

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

      What companies use Gym?
      What companies use scikit-learn?
      See which teams inside your own company are using Gym or scikit-learn.
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      What tools integrate with Gym?
      What tools integrate with scikit-learn?

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      What are some alternatives to Gym and scikit-learn?
      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.
      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/
      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