Feedforward Neural Networks and Self-Organizing Maps

Feedforward networks (FFNNs) are primarily used for supervised learning tasks, such as classification and regression. In contrast, self-organizing maps (SOMs) are employed for unsupervised learning, particularly for dimensionality reduction and data visualization. FFNNs find applications in image recognition, speech recognition, natural language processing, and various classification and regression tasks. At the same time, SOMs are used for clustering, anomaly detection, data visualization, and exploratory data analysis. They can be used together, with SOMs potentially being used to initialize or preprocess data for an FFNN. The SOM could be used to cluster data before training an FFNN for classification. The architecture and mechanism of the FFNN and the self-organizing map are described, followed by a detailed comparison of these two types of neural networks.