Share your work here ✅

thanks for the add to the huggingface study group! currently building a model to identify different running shoe types, will add it here as soon as i’m done :slight_smile:

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update: finished building my model (after spending about 5-6h on it)!
In the world of running, there are quite a number of running shoe types - as a volunteer coach, I constantly get questions as to which exact model of shoes to buy - because wearing the wrong types of shoes for different workouts could lead to injuries, or it might burn through the effective mileage of the shoe quicker (therefore needing a new replacement in a shorter time period). In reality, shoes can be classified into several major categories:

  1. “everyday” running shoes - regular workhorses that you can do most of your easy runs in
  2. tempo running shoes - shoes for quicker speeds, track workouts, but not to be used for racing
  3. racing running shoes - shoes that are used purely for racing and nothing else (to prolong livespan)
  4. stability / maximal cushion running shoes - shoes that are designed to ‘prevent injury’ [although such claims of whether injury is truly prevented are disputed]
    So I built a running-shoe type classifier! You can find it it here, with the source code here

A very humble customizable “Is a bird or not” exercise where the user can easily customize the image search keywords and number of epochs. Interesting to analyze how error rate changes using different search keywords.

Not sure about how the last part works, but apparently it does. I thought that the last line should be:

print(f"Probability it is a {keyword1}: {probs[0]:.4f}")

But that does not work as I expected, so I changed to:

print(f"Probability it is NOT a {keyword1}: {probs[0]:.4f}")

Maybe one of the most veteran mates can share some thoughts about it. Thanks in advance!

If you print probs you will see that it’s a size 2 tensor:

it means that the first value is the probability of the item being in the class 0 and the second value is the probability of it being in the class 1, that’s why if you pick probs[0] you will always have the probability of the item being class 0, but you should remove the “NOT” and use

print(f"Probability it is a {keyword1}: {probs[0]:.4f}")

I just ran your notebook and it works well

That was fast and clear. Many thanks, Kamui!

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sorry julio I got confused you should drop the “NOT” as probs[0] is the probability that the target is keyword1
so you should remove the “NOT”

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I got the point. Thanks again!

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my name is frank, i’m from germany, and I realy like this awsome course.

To get an understanding of what happens during backpropagation i have created a small notebook - mainly to understand this delta rule better. I would be interested in feedback from the community - is this correct as I have outlined it or am I wrong here and there.

I made it in colab here is the link - you should open it in colab - in others it looks bad (I use a lot of tables).

then thank you already all who look at this