Loss functions in Deep Learning using Keras
Loss functions in Deep Learning using Keras In Deep Learning, neural networks requires an optimizer and a loss function to configure an efficient model. The purpose of loss functions is to compute the quantity that a model should seek to minimize during training. Loss function are also termed as Cost function. Loss functions are categorized namely, Probabilistic losses and Regression losses. For training the neural network various algorithm are used. To achieve optimization the weights are updated using back propagation and the optimization algorithms are used to reduce errors in the next iteration with weights changed. The score calculated after each evaluation is called the loss Probabilistic Loss: These loss functions are used to identify classification based models Majority used loss functions in this category are Binary Cross Entropy: This function calculates the loss of classification model where the target variable is binary like 0 and 1. Categorical Cross Entr...