About the Part 1 Beginner category

(Jeremy Howard) #1

This category is strictly for discussing topics that we have brought up in class, questions about running the code that we’ve looked at in class, or any other issues related to the lessons we’ve covered already.

The goal is for this category to be approachable and non-intimidating to folks with the minimum prerequisites (no machine learning background, one year of coding experience). In particular, posts should not cover future lessons, topics that aren’t in this course, setting up or using platforms other than AWS, Crestle, or Paperspace, or other topics outside of what we’ve covered in the lessons.

If you have a suggestion for other blog posts, books, etc which specifically cover a topic we’ve looked at in class in a way that is friendly to beginners, feel free to mention it when appropriate!

I have created a thread for each lesson: if you have any questions or comments about that lesson, please use that thread. Feel free to create new threads if your question or comment is more general, or covers some foundational issue like logarithms, ssh, numpy, etc.

(Sarah Lee) #2

Fantastic and thank you! I find the advanced topics interesting & inspirational. However, it is time-consuming to wade through & filter before discovering the threads that are best suited for me at the time I need them.

I am really enjoying the course & appreciate you being so responsive to the needs of all learners regardless of their start place on the timeline. This educational model is exceeding my expectations in every category. It definitely makes me think about more useful applications for AI that will add layers (not replace) to human decision making.

Am also in full admiration for you in maintaining this level of focus and attention & patience despite some obviously significant distractions in your life. Warm wishes and again a THANK YOU.

(Nikhil B ) #3

This is very helpful even for folks who’ve had prior exposure to ML. There are times when I only want to cement my understanding of basic concepts/clarity class topics, and other times when I like to brainstorm/browse what other folks are doing.

(Santhanam Elumalai) #4

@jeremy Currently I am trying to understand the theory vs implementation ie: I am just going through the papers which are already implemented in pytorch to understand the transformation of theory into a code. I started with the Spatial transformers in pytorch http://pytorch.org/tutorials/intermediate/spatial_transformer_tutorial.html. In which they mentioned about the affine matrix which is kind of hard to understand. Could you please explain this in your terms just like max pooling and relu?

Also, I like to know am I in a right direction towards the deep learning?

(Nathan Yee) #5

Thank you for this section Jeremy. I’ve found that the machine learning community can be very hostile, and having a welcoming place like this is great.

(juwe chekwume raphael) #6

Its a good concept and accurate methodology in deep learning. I would encourage participate to read the documentation


Hi Jeremy and all others involved with the courses. As a beginning developer this course thought me a lot; not only about deep learning but at all related subjects as well. Thanks a lot for sharing it and looking forward to the next courses as things really move fast!

(moran) #9

I have a beginner’s question regards path assignment - lesson 1
I downloaded the dogs cats folder and located it on the following path in my local CP:

From location: (fastai) c:\fastai\fastai>
On jupyter notebook, I run
PATH = “data/dogscats/”
BUT os.listdir(PATH) Shows only
instead of dogscats folders

What did I do wrong?

(moran) #10

Got it