Lesson 3: Practical Deep Learning for Coders 2022

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.