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Lesson 7: Practical Deep Learning for Coders 2022

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