We are making the full videos of lessons 9 and 10 of the new “From Deep Learning Foundations to Stable Diffusion” course available as a special preview of the new course! Here’s all the resources for lesson 10:
Hi Jeremy, thanks so much for putting out these videos. I wasn’t able to do the most recent online course and I was wondering if you have an idea (even vaguely) of when we might get the remaining Part 2 lesson videos. Thank you!
it says in the FAQ section: 1. Will a recording of the course sessions be made available after? Registered participants will have immediate access to the recordings. Public access to the course lectures and materials is typically available 2 or 3 months after completion.
So I guess the part 2 videos will be available around end of January or in February.
@jeremy Any hint about when part 2 will be publicly released? I am taking time off work right now to work on part 1 (and implement a few ideas that I have), and I’m hoping that part 2 will be available not too long after I finish the first part.
I saw that you released a preview of part 2, which is awesome! And that you said in September that it will be out publicly in early 2023.
Obviously, no pressure to give any more info if it’s not something you want to share. But if you are comfortable with it, I’d just love to know if it’ll be in the next couple of months or if it will likely be later than that.
Regardless of whether you can give any additional info, I hugely appreciate the time and effort you’ve put into making these amazing courses :). Thanks so much!
Thank you for the time spent on these courses, these courses are the best I’ve come across for my purposes, the practice-oriented approach is very helpful in learning the material.
You help make the world a better place!)
the chunks function you write here is commonly-enough used that something like it is about to be added to the python standard library in the next version. It’s called itertools.batched, and the reference implementation† looks a lot like you were doing with islice:
def batched(iterable, n):
"Batch data into tuples of length n. The last batch may be shorter."
# batched('ABCDEFG', 3) --> ABC DEF G
if n < 1:
raise ValueError('n must be at least one')
it = iter(iterable)
while batch := tuple(islice(it, n)):
yield batch
