Introduction to Deep Learning (CMU)
9 comments
·March 19, 2025meccabrepapa
rottc0dd
There was another hn page where discussion happened on this topic. Please check following comment thread.
https://news.ycombinator.com/item?id=43391604
https://news.ycombinator.com/item?id=43395172
These resources were helpful for me. Note that, [1] and [2] are concerned about systematic understanding rather than hands on. [3] is a hands on exercise to build neural networks from ground up.
1. A fantastic resource and best resourse IMO, for getting probablistic perspective about machine learning from ground up:
https://www.youtube.com/watch?v=2MuDZIAzBMY&list=PLoROMvodv4...
2. Another good free course.
https://work.caltech.edu/telecourse
3. For hands on after getting some knowledge and building things from ground up:
https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxb...
mliker
I recommend checking out this survey of free ML resources: https://www.trybackprop.com/blog/top_ml_learning_resources
No doubt CMU's intro to deep learning course is good, you might find some other goodies in that link too.
janalsncm
I think for someone who hasn’t seen the material at all before it would be a lot for a semester. They don’t know what backpropagation is but by the end will understand a diffusion model? It’s ambitious, I think.
The other thing is this seems to be very CNN heavy. Four lectures on the topic seems like a lot.
Also, I don’t see embeddings explicitly mentioned as a topic. They’re a huge component of industrial research, and creating good embeddings and retrieving them quickly is a topic I feel students should also be exposed to. (Yes, they mention “representation” with autoencoders but quite frankly the code bit is generally not useful for similarity metrics.)
Finally, it would be nice to expose students to multimodal learning. Something like CLIP would be pretty neat to expose students to. It’s a great insight when you realize that you can train projections of multiple modalities into a shared high dimensional space. If they’re going to cover diffusion models certainly complexity isn’t a concern.
mliker
> They don’t know what backpropagation is but by the end will understand a diffusion model?
That seems plausible to learn in a semester long course, especially at an institution like CMU
diebeforei485
> I think for someone who hasn’t seen the material at all before it would be a lot for a semester. They don’t know what backpropagation is but by the end will understand a diffusion model? It’s ambitious, I think.
Welcome to CMU :)
ascarshen
The most valuable part is the assignments and homework. If possible, where can I find the code?
vivzkestrel
in person or remote? open sourced like MIT or closed source? no details are mentioned whatsoever
yamrzou
If you go to Menu > Lectures, you'll find links to the Youtube videos.
I am a 1year experienced software engineer in a small company. I have been learning Machine Learning recently for company's project. Do you recommend me this course? I want to learn the concept systematically.