Pattern recognition in neural networks involves teaching the network to identify and categorize patterns in data. This process begins by feeding the network a large set of examples, called the training dataset, which includes input data and the corresponding labels or correct outputs. As the network processes each example, it attempts to recognize patterns and make predictions. It compares its predictions with the actual results, and if they are incorrect, the network uses backpropagation to adjust its internal parameters, or weights. This enables the network to refine its pattern detection capabilities. Through repeated exposure to the data, the network improves its ability to identify complex patterns, ultimately becoming adept at recognizing patterns in new, unseen data. This ability to generalize from training data makes neural networks valuable for tasks like image classification, language processing, and more.
