Deep Learning - Artificial Neural Networks with Tensorflow - Categorical Cross Entropy

The video tutorial explains the cross entropy loss function used in multi-class classification, focusing on the categorical distribution and its analogy to a die roll. It covers the probability mass function (PMF) and indicator functions, and how maximum likelihood estimation (MLE) is applied. The inefficiencies of one-hot encoding are discussed, leading to an explanation of sparse categorical cross entropy, which is more efficient in TensorFlow by avoiding unnecessary computations.

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