Видео

Great lecture :)

59:46 "Sometimes these formulae may not make sense, but then if you look at them just right, they begin telling their own story, rigth? Every single mathematical term in life tells you a story if you know how to read it" 🤓✨

wow , everything falling in place !!!!!!!!

Wow, breathtaking quality. This series might be the most comprehensive explanation available for deep neural nets, somehow the professor is able wear the students hat and asks the most critical questions every time! Big thanks to everyone involved in making these available.

This was absolutely brilliant. A masterclass in lecture content design. Very well pieced together -> great flow -> Wow moment towards the end -> evokes a lot of curiosity

A great and informative lecture!! Very much appreciated!

best course about deep learning. now 2024 and happy I found it back. well done!

nice lecture!

lecture begins at at 6:02

Best explanation , can't thank enough for uploading these lectures .

Great lecture, thank you!

I struggled with grasping how the dimensions of the filters and data change with the convolutions and pooling, and this video made it clear. Thank you!

Wonderfull lecture! Thank you.

Lecture starts @ 2:26

Lecture starts @ 3:38

Start at 12:42

Lecture starts at 1:15

great carefully thought out original course, was watching this leisurely and didnt realise an hour went by

This is a very good teacher. He knows how to explain things to students very well

Your teaching unravels the exact concept that is missed by most tutors. Thanks for the great lecture ❤

Thank you for great lecture! ps/ The stick you're holding is impressive.

These lectures are some of the best on the 'net along with Andrew Ng's lectures on Deep Learning. Mad props to the instructor who takes the time to go through the concepts. I wish I had access to the quizzes and group discussions.

What does "We have the id hiyore" mean?

Thank you again to Carnegie Mellon University & Bhiksha Raj. I find these lectures fascinating.

Couldn't help but think of 3B1B videos on hamming codes watching this.

Loving this series! Such a talented lecturer.

thanks for sharing ! :) how can I find the rest of the lectures of the bootcam? thanks again for such nice job!

30:12 question: When he says h1, h2 and h3 are k1, k2 and k3 but h1, h2 and h3 are hidden layers of a neural network. Right?

Amazing lecture as usual, thank you! 2 Cents from a German: Nouns (apple, name) start with a capital letter, so you would write "Apfel" and "Name"...but very happy you have chosen German in this example ;-)

'Promosm'

oh wow

lecture starts at 5:42

Thanks.

Excellent derivation

this lecture should come after lecture 23 - i.e. the videos labeled "18" should come before "17"

abraham lincoln and adolf hitler gay military wedding

17:28 why do three rows mean three in_channels? I would expect that to just be the height of the input

Thanks for posting the recitations too, you are truly transforming people's lives like mine ❤️

Thanks.

This is a gem. Made my fundamentals solid . Thanks :)

Thank you for putting this lecture online! This lecture should be number 18 😊

the matrix of l*d is for one word or complete sentence ? can you explain ?

These recitation videos and the gigantic amount of Python content that goes with them are much appreciated by self-learners, thanks TAs

CMU (like other top US colleges) seems to be cashing in on its brand and admitting tons of below average students into its Master's programs

The stock predictor network slide 14 has the output Y(t+6) for the state t+7. I guess it's a typo. ruclips.net/video/2-c1kaxUnmk/видео.html

Best lectures on DL

Can we get the assignments questions (Both Part 1 and Part 2)?

Hi, I was wondering if this term the part 1 of each homework would be open to students outside CMU? As a self learner, I think implementing NN from scratch using PyTorch will be very interesting, but there're very few tutorials on this.