This free CUDA course by Elliot Arledge is worth more than most CS degrees.
12 hours that separate library users from GPU engineers.
I watched senior devs struggle with concepts taught in hour 3.
What makes it different:
No hand-waving. No "just use this library."
You build an MLP trainer FOUR times: → PyTorch (the easy way) → NumPy (getting harder) → C (now we're cooking) → CUDA (chef's kiss)
Same model. Same dataset. Four implementations.
By the end, you understand WHY PyTorch is fast.
The curriculum nobody else teaches:
➡️ GPU architecture (not just "it's parallel")
➡️ Writing kernels that don't suck
➡️ Profiling at kernel AND system level
➡️ When cuBLAS helps (and when it doesn't)
➡️ CUDA vs Triton (the comparison you need)
➡️ PyTorch extensions (actually useful ones)
Real talk:
➡️ After this course, you'll read PyTorch source code and understand it.
➡️ You'll optimize models other engineers can't touch.
➡️ You'll be the person teams hire to make things fast.
12 hours. Free. No excuses.
(I will put the details in the comments.)
This has been a long time coming - excited to announce we have achieved Mission 1: to image the entire Earth’s landmass every day! https://t.co/RYzOKdN72n