neural network
A neural network, in the context of machine learning, is a computational model designed to recognize patterns and make decisions based on input data. It is inspired by the structure and function of the human brain’s biological neural networks. A typical neural network consists of layers of interconnected nodes or neurons, where each node processes input data using a set of weights and biases, and an activation function to determine its output. The network processes information by passing it through these layers from input to output, adjusting the weights and biases based on the difference between its predictions and actual outcomes during a training phase. This adjustment is often performed using a method called gradient descent. Neural networks are capable of learning from data, enabling them to perform tasks such as classification, regression, and even generating new data that resembles the input data[1][3][4][5].
Citations:
[1] https://en.wikipedia.org/wiki/Neural_network_(machine_learning)
[2] https://towardsdatascience.com/a-concise-history-of-neural-networks-2070655d3fec
[3] https://news.mit.edu/2017/explained-neural-networks-deep-learning-0414
[4] https://www.freecodecamp.org/news/deep-learning-neural-networks-explained-in-plain-english/
[5] https://wiki.pathmind.com/neural-network
[7] https://www.simplilearn.com/tutorials/deep-learning-tutorial/what-is-neural-network