This repository provides an overview of various neural network architectures, including Self-Organizing Maps (SOM), Boltzmann Machines, and Autoencoders. Each type of neural network has its unique structure and application areas. Below is a detailed description of each architecture.
Self-Organizing Maps (SOMs) are a type of unsupervised learning network used for clustering and visualizing high-dimensional data.
- Topology Preservation: Maintain the spatial relationships of input data.
- Competitive Learning: Neurons compete to respond to a subset of the input data.
- Clustering
- Data visualization
- Dimensionality reduction
Boltzmann Machines are stochastic neural networks capable of learning internal representations. They consist of visible and hidden units with symmetric connections.
- Energy-Based Model: The network assigns an energy to each configuration of the variables.
- Restricted Boltzmann Machines (RBM): A variant with a restricted topology to facilitate learning.
- Dimensionality reduction
- Collaborative filtering
- Feature learning
Autoencoders are unsupervised neural networks used for learning efficient codings of input data. They consist of an encoder and a decoder.
- Encoder: Maps input data to a lower-dimensional latent space.
- Decoder: Reconstructs the input data from the latent representation.
- Loss Function: Measures the difference between the input and its reconstruction (e.g., Mean Squared Error).
- Data denoising
- Dimensionality reduction
- Anomaly detection