Lesson 6: Practical Deep Learning for Coders 2022

00:00 Review
02:09 TwoR model
04:43 How to create a decision tree
07:02 Gini
10:54 Making a submission
15:52 Bagging
19:06 Random forest introduction
20:09 Creating a random forest
22:38 Feature importance
26:37 Adding trees
29:32 What is OOB
32:08 Model interpretation
35:47 Removing the redundant features
35:59 What does Partial dependence do
39:22 Can you explain why a particular prediction is made
46:07 Can you overfit a random forest
49:03 What is gradient boosting
51:56 Introducing walkthrus
54:28 What does fastkaggle do
1:02:52 fastcore.parallel
1:04:12 item_tfms=Resize(480, method='squish')
1:06:20 Fine-tuning project
1:07:22 Criteria for evaluating models
1:10:22 Should we submit as soon as we can
1:15:15 How to automate the process of sharing kaggle notebooks
1:20:17 AutoML
1:24:16 Why the first model run so slow on Kaggle GPUs
1:27:53 How much better can a new novel architecture improve the accuracy
1:28:33 Convnext
1:31:10 How to iterate the model with padding
1:32:01 What does our data augmentation do to images
1:34:12 How to iterate the model with larger images
1:36:08 pandas indexing
1:38:16 What data-augmentation does tta use?

Transcript thanks to fmussari, gagan, bencoman, mike.moloch on forums.fast.ai
Timestamps based on notes by daniel on forums.fast.ai