PyBrain vs Lobe.ai Comparison | StackShare | StackShare

Lobe.ai vs PyBrain

Overview

PyBrain

Stacks0

Followers6

Votes0

Lobe.ai

Stacks7

Followers21

Votes0

Lobe.ai vs PyBrain: What are the differences?

Lobe.ai: A simple tool for training machine learning models. It helps you train machine learning models with a free, easy to use tool. It has everything you need to bring your machine learning ideas to life. Just show it examples of what you want it to learn, and it automatically trains a custom machine learning model that can be shipped in your app; 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.

Lobe.ai and PyBrain can be categorized as "Machine Learning" tools.

Some of the features offered by Lobe.ai are:

Machine learning made easy
Free and Private
Ship Anywhere

On the other hand, PyBrain provides the following key features:

Supervised Learning
Unsupervised Learning
Reinforcement Learning

Detailed Comparison

PyBrain	Lobe.ai
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 helps you train machine learning models with a free, easy to use tool. It has everything you need to bring your machine learning ideas to life. Just show it examples of what you want it to learn, and it automatically trains a custom machine learning model that can be shipped in your app.
Supervised Learning; Unsupervised Learning; Reinforcement Learning; Black-box Optimization; Network Architectures; Toy Environments; 3D Environments; Function Environments; Pole-Balancing	Machine learning made easy; Free and Private; Ship Anywhere; Label, Train, Play
Statistics
Stacks 0	Stacks 7
Followers 6	Followers 21
Votes 0	Votes 0
Integrations
Python	No integrations available

What are some alternatives to PyBrain, Lobe.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.

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 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/

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

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

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

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

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.