Category: Neural Networks

One particular kind of deep learning algorithm made specifically for image recognition and classification applications is called a convolutional neural network (CNN). With input images, CNNs automatically deduce the spatial hierarchies of features, drawing inspiration from the human visual system. Three primary parts make up a CNN’s architecture: fully connected layers, pooling layers, and convolutional…

Neural network models called Graph Neural Networks (GNNs) are created especially to handle data that is organized into graphs. Because these networks can accurately represent complex relationships within data, they have become increasingly popular in the fields of machine learning and data analysis. Graph neural networks (GNNs) are designed to process data represented as graphs,…

Recurrent artificial neural networks called Hopfield networks—named for American scientist John Hopfield—are employed to solve optimization and pattern recognition issues. They are able to store and retrieve patterns or memories, acting as associative memory systems. Because these networks are known to converge to stable states, they are useful for tasks involving content addressable memory, image…

A class of artificial neural networks called recurrent neural networks (RNNs) is made to process data sequentially. They are especially useful for tasks involving time series, natural language, and other sequence-based data because of their capacity to preserve internal memory through cyclic connections. RNNs’ hidden state, which is modified at each time step depending on…

Deep Neural Networks (DNNs) are sophisticated artificial neural networks that mimic the operations of the human brain. They are made up of several interconnected layers of nodes, each of which processes and transforms input data to produce output. The structure consists of an output layer that generates conclusions or predictions, an input layer that receives…