Training mechanisms

Training mechanisms refer to how machines learn and improve their performance. They involve feeding a system large amounts of data and adjusting its internal processes based on mistakes it makes. This adjustment, called optimization, helps the system better understand patterns and make accurate predictions or decisions. Different training mechanisms, like supervised learning, use labeled examples, while others, like unsupervised learning, find structures in unlabeled data. Essentially, training mechanisms are methods that enable machines to learn from data, improve their accuracy over time, and become more effective at tasks without being explicitly programmed for every specific situation.