Lesson 3 official topic

Part 1 2022
441

Hi,

What is the maximum accuracy you managed to get with the original implementation, i.e. non-ImageDataLoaders with resnet?

I can get only 20% while resnet is consistently above 98%:
https://github.com/oharlem/fastai-lesson3/blob/main/fastai_lesson3_custom_learner_full_MNIST.ipynb

Please advise!

Ty
D.

442

Hello there,

I tried to redo the Excel titanic example on kaggle notebook sharing it here. Looking for some feedbacks / comments

Thanks

443

I was also confused, so I went back to the workbook to look at the actual python definitions:

def mae(preds, acts): return (torch.abs(preds-acts)).mean()

def quad_mae(params):
    f = mk_quad(*params)
    return mae(f(x), y)

# earlier we defined y as a list of noisy measurements
# y = add_noise(f(x), 0.15, 1.5)

I think confusion might arise (as it did for me) because quad_mae uses y (which is all of the noisy measurements) without it needing to be passed in as a parameter.
Hope this helps!

444

Hi. As an exercise for chapter 4 of the book I’ve tried a simple model to classify the full MNIST dataset. I’m getting about 92% with logistic regression and above 95% with a two layer model. Feedback is more than welcome: Fastbook ch4 MNIST complete | Kaggle

Thanks

445

Hello I have watched the Karpathy`s video that was mentioned here and made the article/notes from it, so if anyone is interested in reading itI post the link. The video itself is really great source and I recommend it as well. Link: Understanding Backpropagation and Chain Rule Through Micrograd and simple Netflix example: A Beginner’s Guide Inspired by Andrej Karpathy’s Tutorial | by Dag | Sep, 2024 | Medium

1 Like
446

I hope this is okay to share here… I found an issue that might be due to a new version of matplotlib regarding the matplotlib.rc?


#hide_output
im3_t = tensor(im3)
df = pd.DataFrame(im3_t[4:15,4:22])
df.style.set_properties(**{'font-size':'6pt'}).background_gradient('Greys')

This code gives an attribute error (ColormapRegistry object has no attribute ‘get_cmap’).

The workaround that I found is the following:

plt.rcParams["image.cmap"] = 'Blues'
df.style.background_gradient(cmap=None)

I hope this helps anybody else who encountered a similar struggle :slight_smile:

450

2025 study group, anyone?

These concepts are no less relevant in the era of vibe coding. I am looking to make this learning experience more social and evenly-paced, if anybody would like to join me. I live in a UTC-4 timezone.

451

If anyone looking for a refresher in calculus before doing the book chapter or even this lecture
3b1b
have a good playlist, the first 2 chapters is enough. You could watch it all would give a lot of intuition and visual representations.