Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN What is Loss Function

The video tutorial explains the concept of one-hot encoding for class labels, where each class is represented by a vector with a single high (1) value and the rest low (0). It then discusses how predicted labels are presented as probability vectors using softmax. The tutorial further details the calculation of cross entropy loss, which measures the difference between predicted and true labels. Finally, it compares cross entropy loss with binary cross entropy loss, emphasizing the importance of choosing the right loss function as a hyperparameter for model performance.

What is a one hot vector and how is it used to represent target labels?

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What is the significance of the index in a one hot vector representation?

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Explain how to compute the cross entropy loss using a predicted label and a true label.

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Describe the difference between cross entropy loss and binary cross entropy loss.

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Why is the choice of loss function considered a hyperparameter in machine learning?

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