Artificial Neural Networks Theory
Artificial Neural Networks Theory involves the study of mathematical models inspired by biological neural networks, which are used in machine learning and artificial intelligence. This theory focuses on understanding how these artificial networks are structured, how they process information, and how they can be trained to perform tasks such as pattern recognition, classification, and prediction. Key concepts in this field include neuron activation functions, network architecture design, learning algorithms such as backpropagation, and optimization techniques for improving network performance.