Lesson 1 In-Class Discussion ✅

i meant is it being shown for validation or training set?

We need some docs to read about these functions he’s describing!

We are, it’s due to a major outage of GitHub yesterday. It’ll be fixed soon.

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That depends. If val_loss > train_loss you are overfitting. If train_loss < val_loss you are underfitting. It is usually good to overfit a little bit in the first epochs, you can always regularize more later.

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You are :wink:

train - what we train with
valid- classified data how are we doing during training
test-un clasified data, real data

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fastai learn.fit_one_cycle API implemented Leslie Smith’s paper:

Paper published on 26 Mar 2018.

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Yes. github was broken for 24 hours and has just recovered but our site didn’t. will be fixed tonight.

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docs.fast.ai

Is it possible to see the details of the error rate? For example false positive vs false negatives?

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valid_loss can be bigger due to dropout being used in training(prevent overfitting). Jeremy will probably explain that later

It will fetch pytorch v0.4.1 but you’ll still have torch-nightly needed for latest fastai

(py_36_env) ⏣ 0:code:$ pip list | grep torch
torch                             0.4.1
torch-nightly                     1.0.0.dev20181022
torchvision                       0.2.1
torchvision-nightly               0.2.1

I tried to clone the repo https://github.com/fastai/course-v3 and doesnt seem to working. Am I missing something?

so, all the blocks except the last (classification block) would be the same? did I understand that correct?

thanks
.K.

what would be the minimal error rate of fine grained cats vs. dogs, if you use a fully trained backbone and something better than resnet34?

one quick questio we may do lot of good thigns in FAI… but are organization comfortable deploying FAI code to the prod… Most of them tend to work with conventional framweworks like pytorch,keras,tensorflows etc…

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http://docs.fast.ai/basic_data does not work. Similarly many other docs.

Hi,
would like to know: how to train from scratch i.e. not using the pretrained weights ?

Please, please turn off the mic!!

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Actually the validation set is some hold out data. It can be by accident that the validation set is a bit easier, so the loss is less.