Hey thanks.

I am not a big fan of notebooks and rarely use them. When I do, I prefer using VS Code notebooks. So maybe I will make a few vids with notebooks in the future, but will likely stick to Neovim.

P.S. As for loss plots, monitoring performance, and those kinds of things, I prefer using tools like WandB, TensorBoard, etc. Will be covering those as well.

Yes, that is how it usually works with my impls! (check out a few vids)

As for mixed precision and metrics - I will be making separate vids for both and of course, for every implemented model, will try to find a dataset to demo train/eval.

It is cool that you mentioned mixed precision as I already have the materials ready for this vid - will be discussing mixed precision, quantization (post-training and quantization aware training), pruning, etc. Improving perf!

It is on the list so will definitely get to it!
Starting next week, will be working on DL impls and vids.

Yeah, I will get to those eventually. For now, want to make some vids and impls of DL models.

Thanks! Yeah, that is definitely an option! I will probably have to split it up into several videos. Also falls into the transformer category.

Yup, all implementations are numerically stable.

Note that I do not discuss numerical stability issues for all activation functions, but for those where the intuitive implementation is not numerically stable (i.e., Sigmoid, Tanh).

I also have a separate video discussing numerical stability: AI/ML Model API Design and Numerical Stability (follow-up). But this is in the context of Gaussian Naive Bayes.

Thank you!
Yup, that is the plan! Will likely make a few more series (about gradient descent, optimizers, etc.) first. We need these for DL and if someone asks how things work, I could then cite the appropriate video series. After that, will dive into deep learning.

Thank you!

Hey thanks for the kind words!
Will be making more AI/ML YouTube series in the future - in fact, working on one as we speak!