PyBrain – Overview

In this article we will undergo basic concepts of the PyBrain package in python,First, we’ll give a brief overview of the function, then discuss its capabilities and functions, then dive deep into specific concepts like neural network data sets and trainers, then we’ll conclude by discussing the workflow PyBrain with advantages and disadvantages.

PyBrain

PyBrain stands for Python-Based Reinforcement Learning, Artificial Intelligence, and Neural Networks Library. It is a modular machine learning library n python that contains very powerful and easy-to-use algorithms used to aid in a variety of machine learning tasks.

Some of the key capabilities of PyBrain is machine learning which has capabilities of black-box optimization and reinforcement learning.

• The first major feature of PyBrain is that it supports neural networks. Neural networks are algorithms built to replicate the human brain to find relationships between data. It is related to feed-forward networks and recurrent networks. In feed-forward networks, information between the nodes is always traveling forward between each of the major components which are the input layer the hidden also known as the computational layer, and the output layer. information is passed from the input layer directly to the output layer. feed foreign networks are the simplest types of neural networks with regards to our current networks they’re very similar to feed-forward networks except the data is saved at each step
• The next major concept in a PyBrain is supervised and classification datasets so supervised data sets have two inputs which are the input and the target these are utilized for supervised learning tasks they’re the simplest kind of data set in addition to supervised classification data sets and these datasets are mostly used for classification problems so, for example, determining who is likely to survive the titanic syncing determining a given fruit-based off of basic features etc. The classification data set has the same input and target as the additional class area and this class area is the one that belongs to the class observation.
• Trainers in PyBrain mainly are backpropagation and train until convergence.

1. Back Propagation Trainer

• Trains the parameters of a module according to a dataset (Supervised or Classification) by back propagating the errors
• BackPropTrainer()

2. Train Until Convergence

• Trains the module of a dataset until it converges
• TrainUntilConvergence()

Workflow of PyBrain

The workflow starts with raw data and then goes through some pre-processing after that the data is divided into groups for training and a network is created for testing and training once the data set is created by the data set trainer is given to. The trainer then trains the data on the network and then trains the data on the network and classifies the output as trained error and validation error which can then be viewed in Python using other libraries such as matplotlib or pyplot and then the last step is to validate the data to see if the output is aligned with the trained data.

Advantages 

• Highly powerful and easy-to-use machine learning package that has a lot of capabilities and was a lot of fun working with this one.
• Great for people just starting out with machine learning.
• Easy to integrate with other Python libraries (Mathplotlib or Pyplot) to visualize data.
• Training and testing data is easy through PyBrain trainers.

Disadvantages

• There is little or no help when a problem arises
• PyBrain hasn’t been updated recently and there are limited resources to help if user encounter a problem.