Based on the two statements provided choose the correct option? Statement 1: Optimization’s primary goal is to reduce the training error. Statement 2: Regularization helps to prevent overfitting, which in turn reduces the training error.

What is the output of the convolution layer after applying ReLU?

The CBOW model is now used to generate a “sentence” or a string of words. First we pass the word “good” and retain the word with highest probability as the output, say word_1, which is in turn passed as input to the model. If the model is run this way for exactly three time steps, what is the sentence that it outputs? Note that the sentence here is “word_1 word_2 word_3”.

Now consider updating the word embeddings using the sample “good good”. The first “good” in the string is used as context and the second “good” as the true label. Use cross entropy as the loss function and run one iteration of gradient descent with η = 1 starting with the existing values for the embeddings. Find the updated word embedding for “good” and choose the most appropriate option from below. Note that you have to compute the updated word embedding for “good” and not its context embedding.

Find s1.

Find s2.