Explore why quadratic cost functions hinder neural network training and how cross-entropy improves learning efficiency in deep learning models.
One of the most common question asked during deep learning knowledge interviews is - “Why can’t we use a quadratic cost function to train a Neural Network?”. In this article, we’ll discuss the answer to this question. There will be a lot of Math involved but nothing crazy! I will keep things simple and precise.
Why can’t we use a quadratic cost function to train a Neural Network? Let’s start with contemplating the general architecture of a Neural network Neural network The General Architecture of a Neural Network We have a series of inputs forming an “input layer”, a series of neurons in the “hidden layer” and one neuron forming an “output layer” for a binary classification problem.
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