00:00 Introduction and survey
01:36 "Lesson 0" How to fast.ai
02:25 How to do a fastai lesson
04:28 How to not self-study
05:28 Highest voted student work
07:56 Pets breeds detector
08:52 Paperspace
10:16 JupyterLab
12:11 Make a better pet detector
13:47 Comparison of all (image) models
15:49 Try out new models
19:22 Get the categories of a model
20:40 What’s in the model
21:23 What does model architecture look like
22:15 Parameters of a model
23:36 Create a general quadratic function
27:20 Fit a function by good hands and eyes
30:58 Loss functions
33:39 Automate the search of parameters for better loss
42:45 The mathematical functions
43:18 ReLu: Rectified linear function
45:17 Infinitely complex function
49:21 A chart of all image models compared
52:11 Do I have enough data?
54:56 Interpret gradients in unit?
56:23 Learning rate
1:00:14 Matrix multiplication
1:04:22 Build a regression model in spreadsheet
1:16:18 Build a neuralnet by adding two regression models
1:18:31 Matrix multiplication makes training faster
1:21:01 Watch out! it’s chapter 4
1:22:31 Create dummy variables of 3 classes
1:23:34 Taste NLP
1:27:29 fastai NLP library vs Hugging Face library
1:28:54 Homework to prepare you for the next lesson

Many thanks to bencoman, wyquek, Raymond Wu, and fmussari on forums.fast.ai for writing the transcript.

Timestamps thanks to "Daniel 深度碎片" on forums.fast.ai.