Lesson 3: Practical Deep Learning for Coders 2022

I think one way to improve the slow/fast issue is that it is actually sometimes, both. The part that needs to go faster, would/should be going faster, or trimmed out unnecessary part.
The parts that is complicated, maybe slow down a bit.
Then add very short/fast "teaching" for each topic, and then goes into details after each short teaching, short teaching is not summary. So people who gets it can move ahead to the next topic.

Skip 10 minutes to start the lesson

i am in love with this course

I couldn't understand why ReLu was needed and now I understand. I'm a programmer and I think this is the DL course for me. The explanation is very easy to understand. Thank you!

=IF([@Embarked]="S" , 1, 0) and other IF statements like this seem not to work for me.
Anyone experienced the same thing.

how much the difference betewen train_loss and validation_loss should be accepted ?

Simply amazing! Excellent lecture.

The excel example blew my mind. Loved this lesson. Thank you.

I was lucky to have good math teachers in high school. Jeremy explaining the concepts reminded me of them. Thanks.

I just made a NN in Excel. Wow. If you want to predict two different things, do you just have a separate set of weights and Lins for the second item?

Where can I find the walk through of Gradio?

So paperspace appears to not be free. When I try starting a notebook he forces me to upgrade to 8/month. Is this still the recommended platform? IS it worth it?

Love the explanation of RELUs being the foundation of learning. So intuitive that you cant forget it or unsee it from the moment you have seen it. ❤

Thank you so much jeremy for making this course, I am going slow but learning a lot everyday, you are a very patient teacher. Thank you.

what a great lesson. mind blown! Thank you so much! You are a great teacher!

I'm slightly confused about the intuition behind how multiple ReLUs can lead to a squiggly line. Wouldn't it more specifically lead to a line that is always either stagnant or gradually increasing because of how the output must be >=0 ?

I am a newbie in machine learning. But the approach, you took in this lesson to explain difficult concepts, is making it so easy to understand. Great work.

Quadratic example was just superb. 🎉

Great lesson!! Jeremy deciding to approach chapter 4 differently after seeing many student quit at this point really shows that he cares about students' learning. Greatly appreciated for the effort!🙏

I "knew" that deep learning models used the sum of wi +xi + b function, I "knew" that it supposedly was used because it was an "all purpose" function, but now thanks to you Jeremy I know WHY its an "all purpose" function
10/10 explanation. Math should always be explained like this, its actually beautiful to see it all unfold.

Wow, great explanation! Thanks!

This is mind blowing! Great job explaining all these concepts.

Unbelievable content! Thanks to all who have made it possible!

basically we have data, now let's create a general function (from those data) that can kind of produce those data and also predict what the next data would be.

👏👏👏 applause from online

I tried to make a Paperspace account and accidentally mistyped the phone verification, so they decided that I'm no longer allowed to verify with my phone number. Disappointing.

I've gone through many great courses in all sorts of subjects, but I think this course might be the best. Kudos for putting out this fantastic content out there for free for everyone to learn.

This is god-tier educational content, sir. Thanks for sharing it!

I don't even know how to use Excel.

As always, an excellent video Jeremy.

Great foundational lecture. Jeremy has a relaxed, non-intimidating approach that works for me. Brilliant step by step walk into the deep end of the pool without getting us lost or scared :) Thank you for taking the time to put this together.

Excellent tutorial! I have one question, in the excel, why are Parch and SibSp not normalized? Because they are not "big enough" to negatively interfere?

The quadratic example was a really good illustration of how gradient descent works - it is really good for building intuition. Then, the Excel example cements the understanding really well with a solid dataset. This is my favourite of the 3 lectures so far.

I don't quite see how the Excel example qualifies as a "deep" neural network, since the layers were not stacked on top of each other but added together. The example is still great, though, and I could see how to stack the layers.

New didactic and methodological ideas - like them very much - still a bit rough in execution - but discovers amazing new territory to approach neural networks - deep learning ... well done!

loved the excelTorch!!

great content.

Amazing talk! Thanks thanks thanks! You're doing the machine learning field so much easier to understand, and that's something invaluable.

Excellent!

dear professor, you mentioned in the learning rate segment when you were drawing there's a "theory" that says everything is quadratic from a certain resolution ongoing. can you please share a link toward which paper introduced this idea?

The quadratic section is a beautifully crafted example. Thanks

Thanks Jeremy, great tutorial.

Thanks! Jeremy, great Lecture, never got into NPL, but now I am understanding it.