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A modular Machine Learning Library for Python
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What is PyBrain?

It's goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms.
PyBrain is a tool in the Machine Learning Tools category of a tech stack.

PyBrain Integrations

PyBrain's Features

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Black-box Optimization
  • Network Architectures
  • Toy Environments
  • 3D Environments
  • Function Environments
  • Pole-Balancing

PyBrain Alternatives & Comparisons

What are some alternatives to PyBrain?
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 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.
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.
See all alternatives

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