00:00 - Tweaking first and last layers
02:47 - What are the benefits of using larger models
05:58 - Understanding GPU memory usage
08:04 - What is GradientAccumulation?
20:52 - How to run all the models with specifications
22:55 - Ensembling
37:51 - Multi-target models
41:24 - What does `F.cross_entropy` do
45:43 - When do you use softmax and when not to?
46:15 - Cross_entropy loss
49:53 - How to calculate binary-cross-entropy
52:19 - Two versions of cross-entropy in pytorch
54:24 - How to create a learner for prediction two targets
1:02:00 - Collaborative filtering deep dive
1:08:55 - What are latent factors?
1:11:28 - Dot product model
1:18:37 - What is embedding
1:22:18 - How do you choose the number of latent factors
1:27:13 - How to build a collaborative filtering model from scratch
1:29:57 - How to understand the `forward` function
1:32:47 - Adding a bias term
1:34:29 - Model interpretation
1:39:06 - What is weight decay and How does it help
1:43:47 - What is regularization
Transcript thanks to nikem, fmussari, wyquek, bencoman, and gagan from forums.fast.ai
Timestamps based on notes by Daniel from forums.fast.ai