Part 2 Lesson 8 wiki

(Jeremy Howard (Admin)) #1

Wiki: Lesson 9 >>>

Lesson resources:


  • Quick summary of how to download the dataset:
     cd ~/fastai/courses/dl2
     ln -s ~/data data && cd $_
     mkdir pascal && cd $_
     curl -OL
     curl -OL
     tar -xf VOCtrainval_06-Nov-2007.tar
     mv PASCAL_VOC/*.json .
     rmdir PASCAL_VOC

Returning to AWS?

  • Login to AWS and get your public IP (xx.xx.xx.xx). Then, follow commands:
ssh ubuntu@xx.xx.xx.xx -L8888:localhost:8888
git pull
conda env update
jupyter notebook
  • For PyCharm and Mac users - a list of the shortcuts Jeremy provided for Visual Studio Code:
    • Action (PyCharm + Mac shortcut)
    • Command palette- (Shift + Command + A)
    • Select interpreter (for fastai env) - (Shift+Command+A) and then look for “interpreter”
    • Select terminal shell- (Shift+Command+A) and then look for “terminal shell”
    • Go to symbol (Option + Command + O)
    • Find references (Command+ G)(go down in the references) (Command + Shift + G) (go up)(Command + Function + F7) (look for all)
    • Go to definition (Command + Down Arrow Key)
    • Go back (Command + [ )
    • View documentation (Option + Space) for viewing source and (Function + F1) for viewing documentation
    • Zen mode (Control + Command + F) and same to get out too
    • Hide sidebar (Command + 1) redoing it will bring it back
    • Find them all with the (Shift + Command+ A) palette option for reference.

Time line for videos

Resources related to stuff that Jermey mentioned we can learn/Homework

  • Python debugger (pdb cheat sheet):
    • You can use the python debugger pdb to step through code:
      • pdb.set_trace() to set a breakpoint
      • %debug magic to trace an error
    • Commands you need to know:
      • s - step: execute and step into function
      • n - next: execute current line
      • c - continue: continue execution until next breakpoint
      • u - up: move one level up in the stack trace
      • d - down: move one level down in the stack trace
      • p - print: print variable, example: “p x” prints variable x
      • l - list: lists 11 lines of code around the current line
  • Alternative debugger that is more easy to use (
    It’s more intuitive than the recommended by the course, But it’s still in developing phase.
    • Demo of debugger:
    • Install: pip install pixiedust
    • Import in top of the juputer notebook: import pixiedust
      *In the top of the cell you like to debugg, type: %%pixie_debugger and run the cell.
    • Use the interactive buttons to step your code even outside of the jupyter notebook.
  • OO Matplotlib
  • Learn Greek letters / List of mathematical symbols at Wikipedia
  • Editor
  • Jupyter Lab video


This is a wiki thread - feel free to add links to resources covered in, or relevant to, the lesson. If you have questions or comments during class, add them below.

Part 2 Lesson 9 wiki
Deep Learning Brasília - Lição 8
Deep Learning Brasília - Lição 8
Deep Learning Brasília - Lição 8
About the Part 2 & Alumni (2018) category
(Jeremy Howard (Admin)) pinned #4

(unknown) #16

Let’s do this! I’m so excited to be part of this again. Best class I’ve ever had. Thank you for making this available to the world - it is amazing.

(William Horton) #28

All set up in NYC!

(ecdrid) #53

Wrote a answer on Stack - DataScience explaining Transfer Learning ( thanks to Jeremy)

Read it here if you like…

(Matt Trent) #58

Can you share the resource you saw the PCI-e profiling of x8 vs x16?

(Vikas Bahirwani) #59

Here’s a good link for Greek Alphabets . Please reply if you have a better link.

(Kartik Godawat) #60

This one contains a couple of benchmarks run on TitanX

(Vikas Bahirwani) #61

Jeremy mentioned developing parts of this course on Windows.

I would love references to his Windows Setup (pytorch and fastai libraries on Windows?).


(Emil) #64

Here is a quick summary for downloading the dataset:

cd ~/fastai/courses/dl2
ln -s ~/data data && cd $_
mkdir pascal && cd $_
curl -OL
curl -OL
tar -xf VOCtrainval_06-Nov-2007.tar
mv PASCAL_VOC/*.json .

Deep Learning Brasília - Lição 8
(Jaya) #65

Can one access the pascal dataset from the website instead of downloading it? Can Path() do it?

(Matt Trent) #67

Why did you choose upper-left + lower-right instead of upper-left + dimensions?

(unknown) #68

Thank you for posting this. Where did you get the google link with the .Json files? I seemed to have missed that one.

(Emil) #69

Great blog post about object-oriented matplotlib API:

(unknown) #70

Found it, it is after the PATH command. Still wondering where the original xml files are though

(KRO) #71

In case anyone likes to solidify things with a song, you can sing the greek alphabet to the tune of the song “row row row your boat”. :slight_smile:

(Emil) #72

Actually, there is a link in one of the Markdown cells with description, after list(PATH.iterdir()). It’s easily missable. I, too, didn’t see it immediately.

(Ankit Goila) #73

I believe working with/specifying 2 parameters is less cumbersome than 3?

(Pavel Surmenok) #74

Does using bottom-left corner instead of width/height of the box impact accuracy of the model?

(chunduri) #75

How is bounding box identified in this example. Which algorithm is used, ie YOLO, R-CNN?