Also referred to as “loss” or “error,” cost function is a measure to evaluate how good your model’s performance is. It’s used to compute the error of the output layer during backpropagation. We push that error backward through the neural network and use that during the different training functions.
In optmization problems, cost function is referred to as an objective functino you are trying to extremize. In deep learning model, its the same. The model tries to minimize the loss function and BP is a way to achieve the same. You dont push the error backward, you compute the gradient backwards to find the updated weights and biases that will lead to minimization of cost function.
Muhammad Imad The cost function is a mathematical function that calculates the difference between a model's anticipated and actual output. It is used to improve the parameters of machine learning models during the training process, especially in supervised learning. The training aims to reduce the cost function, resulting in a more accurate model. It is sometimes referred to as the loss function or the error function.
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