Word Embeddings Word2Vec LSTM, Recurrent Neural Network, GRU Review and Notes - Udacity Deep Learning Nanodegree Part 2

Word embedding can use math to represent relations between words such as man and woman, work and worked

Embedding Weights will be learned while training

Embedding lookup finding the corresponding row in the embedding layer

Embedding dimensions is the number of hidden units

Encode each word as an Integer

Embedding matrix is a weight matrix

Embedding layer is a hidden layer 

Each row of the learned embedding matrix is a vector representation of the input word

The column of the embedding matrix is the number of stacked hidden units? Usually in the hundreds?

Words in similar context, expected to have similar embeddings, such as I drink water throughout the day, I drink coffee in the morning, I drink tea in the afternoon.

such as water, coffee, and tea

such as morning, throughout the day, afternoon

Vector artihmetic 

map a verb A from present to past

map a verb B from present to past

should be the same embedding weights, or vector transformation