The Word2vec algorithm is used to compute the vector representations of words from an input dataset.
There are six parameters that have to be considered:
embedding_size: Denotes the dimension of the embedding vector
max_vocabulary_size: Denotes the total number of unique words in the vocabulary
min_occurrence: Removes all words that do not appear at least ‘n’ number of times
skip_window: Denotes words to be considered or not for processing
num_skips: Denotes the number of times you can reuse an input to generate a label
num_sampled: Denotes the number of negative examples to sample from the input
This next one on the top TensorFlow interview questions and answers blog discusses a tricky question, so pay attention!