Deep-Learning-Algorithm

• Artificial Neural Network (ANN)

  • ANNs are computational models inspired by the human brain. They consist of layers of neurons that hierarchically process data. ANNs can be used for various tasks, such as classification, regression, and pattern recognition.

• Convolutional Neural Network (CNN)

  • CNN is a deep learning model primarily used to process grid-like data such as images. It uses convolutional layers to learn spatial hierarchies of features automatically.

• Recurrent Neural Network (RNN)

  • RNNs are a neural network designed for sequential data, where node connections form cycles. It is well-suited for tasks like time series prediction, natural language processing, and speech recognition, as it can capture temporal dependencies.

• Self-Organizing Map (SOM)

  • SOM is A type of unsupervised learning algorithm that uses neural networks to map high-dimensional data into lower-dimensional (typically 2D) grids. As it preserves the topological structure of the data, SOM is often used for clustering, data visualization, and dimensionality reduction. It helps identify patterns, similarities, and clusters within complex data sets.