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 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, on Timestamps based on notes by daniel on