摘要Summaries--课时二(Lesson 2)

Daniel 深度碎片 on forums.fast.ai has been kind enough to create summaries, in the form of a list of questions, of every lesson. You can use these summaries to remind yourself what you learned in each lesson, or to preview a lesson before you watch it. Here’s the lesson 2 summary:

New exciting content to come
- Can there be substantial new content given we have already 4 versions and a book?
Ways of reading the book
- How many channels available for us to read the book? (physical, github, colab and others)
Extra sweets from the book
- Are there interesting materials/stories covered by the book not the lecture?
- Where can you find questionnaires and quizzes of the lectures?
aiquizzes.com
- Where can you get more quizzes of fastai and memorize them forever?
Introducing the forum
- How to make the most out of fastai forum?
Students’ works after week 1
A Wow moment
- Will we learn to put model in production today?
Find a problem and some data
- What is the first step before building a model?
Access to the magics of Jupyter notebook
- Do you want to navigate the notebook with a TOC?
- How about collapsable sections?
- How about moving between start and end of sections fast?
- How to install jupyter extensions
Download and clean your data
- Why use ggd rather than bing for searching and downloading images?
- How to clean/remove broken images?
Get to docs quickly
- How to get basic info, source code, full docs on fastai codes quickly?
Resize your data before training
- How can you specify the resize options to your data?
- Why should we always use RandomResizedCrop and aug_transforms together?
- How RandomResizedCrop and aug_transforms differ?
Data images instantly transformed not copied
- When resized, are we making many copies of the image?
More epochs for fancy resize
- How many epochs do we usually go when using RandomResizedCrop and aug_transforms?
Confusion matrix: where do models get wrong the most?
- How to create confusion matrix on your model performance?
- When to use confusion matrix? (category)-practice
- How to interpret confusion matrix?
- What is the most obvious thing does it tell us?
- How hard is it to tell grizzly and black bears apart?
Check out images with worse predictions
- Do plot_top_losses give us the images with highest losses?
- Are those images merely ones the model made confidently wrong prediction?-practice
- Do those images include ones that the model made right prediction unconfidently?
- What does looking at those high loss images help? (get expert examination or simple data cleaning)
What if you want to clean the data a little
- How to display and make cleaning choices on each of those top loss images in each data folder?-practice
- Without expert knowledge on telling apart grizzly and black bears, at least we can clean images which mess up teddy bears.
Myth breaker: train model and then clean data
- How can training the model help us see the problem of dataset?-practice
- Won’t we have more ideas to improve the dataset once we spot the problems of the dataset?
Turn off GPU when not using
- How to use GPU RAM locally without much trouble?
Watch first, then watch and code along
- What is the preferred way of lecture watching and coding by majority of students?
A Gradio + hugging face tutorial
Git and Github desk
- Is Github desk a less cool but easier and more robust way to version control than git?
Terminal for windows
- How to set up terminal for windows?
- Why Jeremy prefer windows than mac?
Get started with Hugging Face Spaces
- go to huggingface.co/spaces and create a new space
Get the default App up and running
- How to use git to download your space folder?
- How to open vscode to add app.py file?
- How to use vscode to push your space folder up to hugging face spaces online?
- then go back to your space on Hugging Face to see the app running
Train and download your model
- Where is the model we are going to train and download from Kaggle notebook?
- How to export your model after trained it on Kaggle?
- Where do you download the model?
- How to open a folder in terminal? open .
- Make sure the model is downloaded into its own Hugging Face Space folder
Predict with loaded model
- How to load downloaded model to make prediction?
- How to make prediction with the loaded model?
- How to export selected cells of a jupyter notebook into a python file?
- How to see how long a code runs in a jupyter cell?
Turn your model into Gradio App locally
- How to prepare your prediction result into a form gradio prefers? #code
- How to build a gradio interface for your model?
- How to launch your app with the model locally?
- Not in video: run the code on Kaggle in cloud
Push this app onto Hugging Face Spaces
- Make sure to create a new space first, e.g., testing
- How to turn the notebook into a python script?
- How to push the folder up to github and run app in cloud?
- Not in Video: if stuck, check out Tanishq tutorial-shooting
How many epochs are ideal for fine tuning?
How to save model from colab?
How to install fastai properly
- How to download github/fastai/fastsetup using git? git clone https://github.com/fastai/fastsetup.git
- How to download and install mamba? ./setup_conda.sh
- Not in Video: problem of running ./setup_conda.sh
- How to download and install fastai? mamba install -c fastchan fastai
- How to install nbdev? mamba install -c fastchan nbdev
- How to start to use jupyter notebook? jupyter notebook --no-browser
- Not in Video: other problem related to xcode
The workflow summary
HuggingFace API + gradio + Javascript = real APP
How easy does HuggingFace API work
How easy to to get started with JS + HF API + gradio
App example of having multiple inputs and outputs
App example of combining two models
How to turn your model into your own web App with fastpages
How to fork a public fastpages for your own use