PyBrain supports various types of neural network architectures, allowing users to construct and customize networks for different tasks and applications. Some of the commonly supported neural network architectures in PyBrain include:
Feedforward Neural Networks (FNN) :
* Feedforward neural networks are the most basic type of neural network architecture, where connections between neurons do not form cycles.
* PyBrain allows users to create feedforward neural networks with customizable numbers of layers, neurons, and activation functions.
Recurrent Neural Networks (RNN) :
* Recurrent neural networks are characterized by feedback connections between neurons, allowing them to process sequences of data with temporal dependencies.
* PyBrain supports recurrent neural networks, including Elman networks, Jordan networks, and more general recurrent network structures.
Convolutional Neural Networks (CNN) :
* Convolutional neural networks are specialized architectures for processing grid-like data, such as images.
* While PyBrain may not provide built-in support for CNNs, users can implement convolutional layers and pooling layers using custom code.
Modular Neural Networks :
* Modular neural networks allow users to construct networks from reusable modules, making it easier to design and experiment with complex architectures.
* PyBrain supports modular neural networks, where users can define custom modules and combine them to create diverse network structures.
Self-Organizing Maps (SOM) :
* Self-organizing maps are a type of unsupervised learning neural network architecture used for clustering and dimensionality reduction.
* PyBrain provides support for self-organizing maps, allowing users to create and train SOMs for various tasks.
Autoencoders :
* Autoencoders are a type of neural network architecture used for feature learning and data compression.
* While PyBrain may not provide specific support for autoencoders, users can implement autoencoder architectures using custom code with feedforward or recurrent networks.
Competitive Networks :
* Competitive networks are a type of unsupervised learning neural network architecture used for clustering and pattern recognition.
* PyBrain supports competitive networks, such as Kohonen self-organizing maps, for tasks involving unsupervised learning and pattern recognition.
