Supervised Learning
Supervised learning under Convolutional Neural Networks refers to the training of neural networks using labeled data, where both input and desired output are provided for each example in the dataset. This technique enables the model to learn patterns within the data by comparing its predictions with the actual outputs during the learning process. The network continuously refines its internal weights and biases until it can accurately predict the expected outcomes given new inputs. In essence, supervised learning under Convolutional Neural Networks is a systematic approach for teaching neural networks how to make accurate predictions based on labeled data samples. It plays a crucial role in various applications such as image recognition and natural language processing, where precise determination of patterns from large datasets is essential for optimal performance.