Neural Networks utilize algorithms, artificial intelligence (AI), and machine learning for pattern recognition in data sets through a process similar to the way neurons signal each other in the human brain, learning from training examples how to automate the performance of tasks. Neural networks depend on training data sets to learn and gain accuracy over a period of time, becoming invaluable tools for computer science and AI and enabling rapid data classification for functions such as speech recognition or image identification. Google’s search algorithm is one of the most commonly recognized neural networks.
Different types of neural networks can be used for different purposes. Neural networks are made up of node layers comprised of an input layer, various hidden layers, and an output layer with each node acting as a neuron signaling other nodes of varying weights and thresholds. A node becomes activated when the value of the output data is outside of its specific threshold. It then sends that data to the next layer in the network, making that node layer's output the input value of the next node. Deep neural networks are traditionally feedforward, with data flowing unidirectionally from input to output and the continuous process of passing data from one layer to the next.
Alternately, models can be trained via backpropagation to transmit data from output to input, allowing for measurement of each neuron's error probability and calculating the necessary model adjustments. Feedforward neural networks are the most common and are the foundation for natural language processing, computer vision, and many other neural networks. Convolutional neural networks are comparable to feedforward networks but are used primarily for image and pattern recognition and computer vision tasks, employing algebraic principles such as matrix multiplication to single out patterns in an image. Recurrent neural networks leverage time-series data to forecast future outcomes, such as stock market or sales predictions.