What are the features of PyBrain

This recipe explains what are the features of PyBrain

Recipe Objective - What are the features of PyBrain?

PyBrain is a modular machine learning library for Python. Its goal is to offer flexible, easy-to-use yet powerful algorithms for machine learning tasks and various predefined environments to test and compare your algorithms.

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The features of Pybrain:

1. Networks:

A network consists of modules that are connected by links. Pybrain supports neural networks like FeedForward Network, Recurrent Network, etc.
* Feedforward Network is a neural network in which information moves between nodes and never goes back. The feedforward network is the first and most straightforward among the networks available in the artificial neural network. Data is transmitted from the input nodes together with the hidden nodes and then to the output node.
* Recurring networks are similar to the feed-through network; the only difference is that you must remember the data at each step. The history of each step must be saved.

2. Trainer:

When we create a network, i. H. a neural network, it trains based on the training data. Whether the network is adequately trained now depends on predicting the test data tested on that network. The most crucial concept in Pybrain Training is the use of BackpropTrainer and TrainUntilConvergence.
* BackpropTrainer: is a trainer that trains the parameters of a module according to a monitored record or ClassificationDataSet (possibly sequentially), propagating the errors backward (through time).
* TrainUntilConvergence: Used to train the module on the dataset until it converges.

3. Datasets:

Data sets are the data that are available for testing, validation, and training in networks. The type of dataset to use depends on the tasks that we will do with machine learning. The most common data sets that Pybrain supports are SupervisedDataSet and ClassificationDataSet.
* SupervisedDataSet - Consists of input and target fields. It is the simplest form of data set and is used primarily for supervised learning tasks.
* ClassificationDataSet - Used primarily to solve classification problems. It accepts input, target field, and an additional field called "class," an automatic backup of the specified targets. For example, the output will be 1 or 0, or the output will be grouped with values based on the given input or fall in a certain class.

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