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

The video tutorial discusses image captioning, focusing on the use of one hot vectors to represent labels. It explains how the length of these vectors corresponds to the vocabulary of the target language. The tutorial also covers the concept of probability vectors and how cross entropy loss is computed using these vectors. Finally, it highlights the similarities in defining loss functions across different architectures.

What is the representation of labels in image captioning?

• One-hot vectors

• Scalar values

• Probability distributions

• Binary vectors

What does the length of a one-hot vector in image captioning correspond to?

• The number of timesteps

• The number of captions

• The number of images

• The size of the vocabulary

What is Yi hat in the context of image captioning?

• A one-hot vector

• A scalar value

• A vector of probabilities

• A binary vector

How is cross-entropy loss computed in this context?

• As the average of all probabilities

• As the product of probabilities

• As the negative log of the probability value at a specific index

• As the sum of probabilities

How are losses aggregated across timesteps in image captioning?

• By averaging them

• By summing them up

• By multiplying them

• By taking the maximum value