Multi-class Problems: One-hot Coding

This lecture covers the distinction between multi-class problems with multiple attributes and those with exclusive classes, focusing on one-hot coding and the cross-entropy loss function. It explains the concept of mutually exclusive classes, the use of one-hot coding for exclusive classes, the importance of sigmoidal output, the derivation of softmax as an optimal multi-class output, and the softmax function. The lecture also discusses the interaction of output units in softmax, the normalization guarantee, and the cross-entropy error for multi-class problems, emphasizing the minimization of cross-entropy under constraints and comparing it with KL divergence.