Chapter 14 - Training with Minimal Data Points

1. How are pre-trained word vectors obtained?
   From an existing database such as GLOVE or word2vec
2. How do we map from an image feature embedding to word embedding in Zero-shot learning?
   By creating a suitable neural network that returns a vector of the same shape as word-embedding and training with mse-loss (comparing prediction with actual word-embedding)
3. Why is the Siamese network called so?
   Because we always produce and compare two outputs with each other, for identicalness. Siamese stands for twins.
4. How does the Siamese network come up with the similarity between the two images?
   The loss function forces the network to predict that the outputs have a smaller distance if the images are similar.