Embedding Layer: Word Representation
Understand the challenges of vocabulary-based word representations and learn how embedding layers create dense, compact vectors. Explore how embedding layers work as trainable matrices that transform sparse inputs into reduced dimension vectors, improving the efficiency of RNNs in NLP projects.
We'll cover the following...
We'll cover the following...
Problem with previous word representations
We already saw that one simple way to create a numeric representation of the text is to create a vocabulary of our dataset and assign a number to each word. However, this has one major problem: there will be too many sparse vectors. This means that, after padding, we could have many vectors that will contain almost more than half of the values as 0s.
Therefore, we will ...