What is Supervised Learning in neural network?

Supervised learning is a machine learning paradigm where an algorithm learns from labeled data to make predictions or decisions. In the context of neural networks, supervised learning involves training a network using input data and corresponding output labels.

Here's how the process typically works:

1. Data Preparation: A dataset is prepared, consisting of input samples and their corresponding output labels. Each input sample is a set of features or attributes, and each output label represents the desired prediction or classification.

2. Network Architecture: A neural network model is designed by defining the number of layers, the number of neurons in each layer, and the activation functions used in those neurons.

3. Training: The network is trained on the labeled data. During training, the network iteratively adjusts its internal parameters (weights and biases) to minimize the difference between its predictions and the true output labels. This optimization process is typically achieved using algorithms like backpropagation and gradient descent.

4. Prediction: After training, the network is capable of making predictions on new, unseen data. Given an input sample, the trained network processes it through its layers and produces an output prediction.

5. Evaluation: The performance of the trained model is assessed by comparing its predictions to the true output labels from a separate validation or test dataset. Common evaluation metrics include accuracy, precision, recall, and F1 score, depending on the nature of the problem being solved.

Supervised learning is widely used in various applications, such as image recognition, speech recognition, natural language processing, and many others. It requires a sufficient amount of labeled data for training and relies on the availability of accurate ground truth labels to guide the learning process.