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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. Gym vs PyBrain

Gym vs PyBrain

OverviewComparisonAlternatives

Overview

PyBrain
PyBrain
Stacks0
Followers6
Votes0
Gym
Gym
Stacks54
Followers59
Votes0
GitHub Stars36.7K
Forks8.7K

PyBrain vs Gym: What are the differences?

What is PyBrain? A modular Machine Learning Library for Python. 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.

What is 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.

PyBrain and Gym belong to "Machine Learning Tools" category of the tech stack.

Gym is an open source tool with 22.1K GitHub stars and 6.26K GitHub forks. Here's a link to Gym's open source repository on GitHub.

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Detailed Comparison

PyBrain
PyBrain
Gym
Gym

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.

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.

Supervised Learning; Unsupervised Learning; Reinforcement Learning; Black-box Optimization; Network Architectures; Toy Environments; 3D Environments; Function Environments; Pole-Balancing
Reinforcement learning; Compatible with any numerical computation library
Statistics
GitHub Stars
-
GitHub Stars
36.7K
GitHub Forks
-
GitHub Forks
8.7K
Stacks
0
Stacks
54
Followers
6
Followers
59
Votes
0
Votes
0
Integrations
Python
Python
Theano
Theano
TensorFlow
TensorFlow

What are some alternatives to PyBrain, Gym?

TensorFlow

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.

scikit-learn

scikit-learn

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

PyTorch

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

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

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

Polyaxon

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

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.

MLflow

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

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