Supervised Learning in Neural Networks is a type of machine learning where the model is trained on a labeled dataset. The dataset consists of input-output pairs, where the input is the feature vector and the output is the corresponding label. The goal of the model is to learn the relationship between the input and output so that it can predict the output for unseen input.
In a supervised learning neural network, the input is passed through a series of layers, which are made up of interconnected nodes or neurons. These layers are called the input, hidden, and output layers. The input layer receives the input data, while the output layer produces the final prediction. The hidden layers are used to extract features and perform non-linear transformations on the input data.
The model is trained using a set of labeled data, where the inputs and outputs are known. The model’s parameters are adjusted to minimize the error between its predictions and the true outputs. This process is called training. Once the model is trained, it can be used to predict the output for new, unseen inputs.
Supervised learning neural networks can be used for a wide range of tasks, including image classification, speech recognition, natural language processing, and many other applications. They are trained using a variety of algorithms such as backpropagation, and the selection of the algorithm depends on the complexity of the task and the size of the dataset.
In summary, Supervised Learning in Neural Networks is a type of machine learning where the model is trained on labeled data to predict the output for unseen inputs. It is a powerful technique that can be used for a wide range of tasks and has been widely adopted in many industries.