Lesson 2: Practical Deep Learning for Coders 2022

00:00 - Introduction
00:55 - Reminder to use the fastai book as a companion to the course
02:36 - Reminder to use fastai forums for links, notebooks, questions, etc.
03:42 - How to efficiently read the forum with summarizations
04:13 - Showing what students have made since last week
06:45 - Putting models into production
08:10 - Jupyter Notebook extensions
09:49 - Gathering images with the Bing/DuckDuckGo
11:10 - How to find information & source code on Python/fastai functions
12:45 - Cleaning the data that we gathered by training a model
13:37 - Explaining various resizing methods
14:50 - RandomResizedCrop explanation
15:50 - Data augmentation
16:57 - Question: Does fastai's data augmentation copy the image multiple times?
18:30 - Training a model so you can clean your data
19:00 - Confusion matrix explanation
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 - Deploying your deep learning model
37:38 - Dog/cat classifier on Kaggle
39:40 - Downloading your model on Kaggle
41:30 - How to take a model you trained to make predictions
44:22 - Shaping the data to deploy to Gradio
45:47 - Creating a Gradio interface
48:25 - Creating a Python script from your notebook with #|export
50:47 - Hugging Face deployed model
52:12 - How many epochs do you train for?
53:16 - How to export and download your model in Google Colab
54:25 - Getting Python, Jupyter notebooks, and fastai running on your local machine
1:00:50 - Comparing deployment platforms: Hugging Face, Gradio, Streamlit
1:02:13 - Hugging Face API
1:05:00 - Jeremy's deployed website example - tinypets
1:08:23 - Get to know your pet example by aabdalla
1:09:44 - Source code explanation
1:11:08 - Github Pages

[zzznavarrete]

Also, whoever is using Gradio's version 4.39 or above, the following is code is for the Gradio interface:

# creating the gradio interface

image = gr.Image(height=192, width=192)
label = gr.Label()
examples:list = ['./assets/dog.png', './assets/cat.png', './assets/dunno.png']

intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False)

[Karen-lshv]

thank you for making AI education free and accessible 😊

[mikementele]

I've taken many ML courses over the years and I love the hands on nature of this with Jupyter notebooks, the extra background provided with the book, the quizzes, and the top down approach that orients you on breadth before depth. This is done right from a pedagogy standpoint.

[saadorj]

Thank you, Jeremy, and everyone involved in creating this great course. It only gets better with every new iteration!

[cojocarucosmin]

Respect for giving out high quality information, you are the real deal and so few people actually know...

[chuhaoliu]

Update for 2023. In Gradio deployment notebook, `intf.launch(inline=False)` needs to change to `intf.launch(inline=False, share=True)` to have the public web link.

[palahnuk]

Jeremy ... you are the real deal ... thanks for giving your personal time and energy to open the world of ML to mere mortals like us older engineers ... you are a real world blessing!

[dmoneyballa]

Thank you so much for adding the commands for wsl to add the drivers on ubuntu. this is the most time consuming part for me when I setup a new computer. I really appreciate it.

[chuhaoliu]

It's way more fun to learn by building something people can actually use!! Thanks a ton!

[KiLVaiDeN]

This is very neat content, and your way of explaining those concepts is calm and posed, very enjoyable to watch ! Thank you.

[zzznavarrete]

for those who are using windows and got the following error message: "NotImplementedError: cannot instantiate 'PosixPath' on your system", the fix is:

import pathlib
temp = pathlib.PosixPath
pathlib.PosixPath = pathlib.WindowsPath

[hiankun]

"And when I say one day, more specifically... today!" XD

Today is a good day. 😀

[AiEdgar]

No fillers so much knowledge I wish all my teachers were this good.

[rahmanoff]

Thanks Jeremy!
p.s. if you try to go step by step and got error "module 'gradio' has no attribute 'inputs'", try gradio==3.48.
It works well for me.

[jasonholtkamp]

You know what's hilarious is that I actually read the book chapter before watching the lesson and I had the hardest time finding my Azure Bing API Key after I made my account and whatnot... It literally took me longer to just do that step than the entire rest of the lesson lol. And then Jeremy just goes yeah screw that I'm going to use duckduckgo instead haha.

[elvryn_]

So fun hearing anecdotes of classifying images of a dog cat with your daughter

[marktomm]

With gradio==4.28.3 the example code in the video won't work.

error "module 'gradio' has no attribute 'inputs'"

Fix:
image = gr.Image(height=192, width = 192)
label = gr.Label()

[estrellaum]

I need triple the time of the video to understand it, but I am getting there, thanks! By the way, the dog-cat thing apparently is a puppy...

[miladrahimi]

This tutorials is really helpful, thanks so much. you are boosting my knowledge.

[arkadymarchenko]

Hi Jeremy, I love the education your giving through your videos and non-coding parts of your book but I often find you notebooks are pretty buggy and down work out of the box. The last notebook provided for lesson 1 (is it a bird) required me to import another image crawler to make it work. For this lesson, the ddg image search wasnt working properly as well and I had to make major modifications. I love everything you're doing but I would love if you updated this course to provided updated and working notebooks to make it seamless for people like me to learn deep learning