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00:00 – Introduction
00:55 – Reminder to use the fastai book to supplement the course
02:06 – aiquizzes.com for quizzes about the book
02:36 – Reminder to use fastai forums for links, notebooks, questions, etc.
03:42 – How to read the forum efficiently with summaries
04:13 – Show what students have created since last week
06:45 – Putting models into production
08:10 – Jupyter Notebook extensions
09:49 – Collecting images with Bing/DuckDuckGo
11:10 – Finding information and source code about Python/fastai functions
12:45 – Cleaning up the data we collected by training a model
13:37 – Explanation of different resizing methods
2:50 PM – RandomResizedCrop explanation
15:50 – Data augmentation
16:57 – Question: Does fastai's data augmentation copy the image multiple times?
18:30 – Train a model so you can clean your data
19:00 – Explanation of the confusion matrix
20:33 – plot_top_losses explanation
22:10 – ImageClassifierCleaner demonstration
25:28 – CPU RAM vs GPU RAM (VRAM)
27:18 – Putting your model into production
30:20 – Git & Github desktop
31:30 – For Windows users
37:00 – Implementing your deep learning model
37:38 – Dog and cat classifier on Kaggle
38:55 – Exporting your model with learn.export
39:40 – Download your model on Kaggle
41:30 – How to use a model you've trained to make predictions
43:30 – learn forecasting and timing
44:22 – Shaping the data for implementation in Gradio
45:47 – Creating a Gradio interface
48:25 – Create a Python script from your notebook using #|export
50:47 – Hug face deployed model
52:12 – How many eras are you training for?
53:16 – How to export and download your model in Google Colab
54:25 – Get Python, Jupyter notebooks, and fastai running on your local machine
1:00:50 – Compare deployment platforms: Hugging Face, Gradio, Streamlit
1:02:13 – Hug Face API
1:05:00 – Jeremy's example of a website: tinypets
1:08:23 – Get to know your favorite example through aabdalla
1:09:44 – Explanation of the source code
1:11:08 – Github pages
Thanks to bencoman, mike.moloch, amr.malik, gagan, fmussari, kurianbenoy and heylara at forums.fast.ai for making the transcription.
Thanks to Raymond-Wu at forums.fast.ai for creating the timestamps.
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