vocab and fine_tuned_enc when create your language model.learn.recorder.plot_losses??
TabularLearner?I wanna join the meetup
@lesscomfortable I’m a little confused about how fitting works? suppose I ran train model for 3 epochs first and then 2 epochs . is that equals to running 5 epochs in one go?
The meetup is on now! Everyone is welcome to join! 
#Meeting Minutes 04-01-2020 (Thanks @msivanes for the inputs)
## Notes
## Advice
## Questions
## Off topic
We decided to rotate the presentation of the lessons among us. As you know, our meetings are informal, so this is basically like explaining the material to friends and boosting your presentation skills in a supportive environment 
For a learner, it is one of the best ways to actively engage with the material and actually learn better by explaining. So choose the lesson that you want to understand better yourself. The lesson’s recap should be short (~15-20 mins) covering main concepts in a simple language. So grab a chance and please write which lesson you would like to present 
Of course, all newcomers are welcome
I started the lessons a couple days ago. just came across this thread! Would love to join the next session and learn from everyone. Thank you for this initiative! 
Just a reminder, the meetup will be held today in ~1h
The meetup is on
There should be a reminder!!! one day prior to the meeting.
Thanks for the feedback. If there’s an automated way to do that, let the group know.
It’s much easier for us as community members to create a personal calendar event with the information shared in the wiki to make that happen. @shahnoza is volunteering the time to host this and kind enough to provide zoom for this study group. I try not to ask the host to do more work than needed.
Thanks for the feedback. Currently, @shahnoza does remind us on the group regarding the meetups. However, based on the feedback, I have now added an automated reminder to the slack group. We should be getting a reminder on Fridays. As @msivanes pointed out, it would be easier for the members to create a personal calendar event.
Announcement about restart of cadence (starting Lesson 2) by @shahnoza
Lesson 2 review by @gagan
Classifier for pen vs pencil followed by questions. @gagan actually timed it starting from data collection to the inference & time taken is 23 min to demonstrate fastai is really FAST AI . (@gagan++) Colab
Conceptual Framework of Supervised Learning (Gradient, Parameters, Loss, Model, Observations, Targets) by @msivanes for lesson2 - sgd.
Projects Showcase
Class Imbalance : Is it still valid when we use transfer learning?. It might due to the fine tuning. The best thing to do is try it out as Jeremy said.
num_workers : number of cpu cores to speed up the data grabbing process. If you are get out of memory error, reduce num_workers to a smaller number or reduce the batch size(bs).
No Classification without Representation showing ImageNet & OpenImages data are imbalanced
@AjayStark
The top post(wiki) has all the information that you need to participate in the study group & in the discussions. Let us know if you face any difficulties with anything specific.
I shared the below image in our previous meetup. This is an updated version along with annotated code and notes.
Let’s walk through step by step and also refer how we code each blocks from the below image
y_hat = model(x) , here we are using own model.loss_func(y_hat, y). In addition to that we are also adding it with w2*wd
parameter.sub_(learning_rate * gradient), performing an inplace subtraction on parameters with product(learning_rate, gradient). But since our model has multiple parameters (weights, biases), we are looping through them using PyTorch parameters.for p in model.parameters(): w2 += (p**2).sum()
losses = [update(x,y,lr) for x,y in data.train_dl]
.item() turns into a python number in order to plot & see them visually.def update(x, y, learning_rate):
wd = 1e-5
#prediction
y_hat = model(x)
w2 = 0.
#sum of squared weights
for p in model.parameters():
w2 = w2 + (p**2).sum()
# regular loss
loss = loss_func(y_hat, y) + w2*wd
# updates the gradients in the model ie parameters
loss.backward()
# instruct pytorch not to record these actions for the next gradient calculation
with torch.no_grad():
for p in model.parameters():
#gradients
p.sub_(learning_rate * p.grad)
p.grad.zero_()
return loss.item()
Feedback is welcome.
Just a reminder, there is a meetup tomorrow at 2pm GMT, dedicated to Lesson 3
