1. Word2Vec

2. Introduction: What is word2vec?
Motivation : Why Word2vec?
Word2vec models:
Continuous Bag of words
Skip gram model
Demo

3. What is Word2Vec? Introduced by Google in 2013
Computes vector representation of words
Word meanings and relationships between words are encoded spatially
Learns from input texts

4. Motivation Images; Speech easily represented in the form of vectors.
What about text?
Word2vec learns word embeddings. Converts words into meaningful vectors.
Basically trains a neural network with a single hidden layer to perform a certain task.
Doesn’t use neural network output, but instead uses the weights learnt.These weights serve as vector representations

5. Contextual Representation
I eat an apple every day.
I eat an orange every day.
I like driving my car to work.
Ref:

6. Word vectors

7. Learning Algorithms
Continuous bag-of-words
Continuous skip gram

8. Continuous Bag-of-words

10. Continuous skip-gram

12. Hidden Layer

13. How do we get word vectors?

14. Output layer

15. How is this different?

17. Demo