This thread contains the in-class discussion from Lesson 6. The Wiki links have been moved to this new thread. Please ask any questions about lesson 6 in the new wiki thread.
Waiting for the link to the lecture 6.
Me too… it is about RNN today, interesting topic
stream working great!
can u republish the link please
This is better than NIPS!
It is on at the top of the topic.
sound cuts every 20 sec or so for a sec
Is this the last lecture?
its getting better
penultimate
I love the jokes Jeremy cracks!
Why do we have to have the extra column instead of just adding another embedding?
What’s the difference between having length 51 vectors and having a length 50 embedding and an extra 1 besides that
Jeremy asked to remind to talk more on using Dropout in training and testing in Pytorch
Thanks!
isn’t the embedding matrices size match properly or broadcasting is used?
EmbeddingDotBias (
(u): Embedding(671, 50)
(i): Embedding(9066, 50)
(ub): Embedding(671, 1)
(ib): Embedding(9066, 1)
)
I guess fastai still doesn’t support CPU?
Do you mean that the number of users and the number of items (movies) are different, or that they have a different number of factors between the user/item embeddings and the bias embeddings?
The model is running for a single user/movie pair, and uses dense layers to map between the embedding and bias values into a single prediction.
It checks out.