Neural Network Training

Neural network training is the process of adjusting the weights and biases of a neural network to minimize the difference between the predicted output and the actual output. This is done by feeding the network with a large amount of labeled data and using an optimization algorithm to update the weights and biases. The goal of neural network training is to achieve a high level of accuracy in predicting the output for new, unseen data. The training process can be supervised, unsupervised, or semi-supervised, depending on the availability of labeled data. Supervised training involves providing the network with input-output pairs, while unsupervised training involves only input data. Semi-supervised training is a combination of both. Neural network training is a computationally intensive task that requires a large amount of data and computing resources.