Data Science and Machine Learning (Theory and Projects) A to Z - RNN Architecture: ManyToMany Model Solution 02

The video tutorial explains the concept of loss functions in machine learning, focusing on named entity recognition with five classes. It covers the use of one-hot encoding for class labels and the role of recurrent neural networks (RNNs) in processing inputs over time. The tutorial details how the softmax layer generates probability vectors and how to compute the loss using cross entropy, emphasizing the importance of calculating the deviation between predicted and true vectors. The video concludes by discussing the aggregation of losses across time steps to form a comprehensive loss function.

What is the significance of the softmax layer in the neural network described?

How is cross-entropy loss calculated in this context?