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Keras vs Gym: What are the differences?
Keras: Deep Learning library for Theano and TensorFlow. Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/; Gym: 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.
Keras and Gym can be categorized as "Machine Learning" tools.
Keras is an open source tool with 48.9K GitHub stars and 18.4K GitHub forks. Here's a link to Keras's open source repository on GitHub.
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Learn MorePros of Gym
Pros of Keras
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Pros of Keras
- Quality Documentation8
- Supports Tensorflow and Theano backends7
- Easy and fast NN prototyping7
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Cons of Gym
Cons of Keras
Cons of Gym
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Cons of Keras
- Hard to debug4
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- No public GitHub repository available -
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 Keras?
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
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Jobs that mention Gym and Keras as a desired skillset
What companies use Gym?
What companies use Keras?
What companies use Gym?
What companies use Keras?
See which teams inside your own company are using Gym or Keras.
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What tools integrate with Gym?
What tools integrate with Keras?
What tools integrate with Gym?
What tools integrate with Keras?
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What are some alternatives to Gym and Keras?
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.
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.
Streamlit
It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.