[HN Gopher] Deep Learning Course
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Deep Learning Course
Author : Tomte
Score : 376 points
Date : 2023-11-19 10:19 UTC (12 hours ago)
(HTM) web link (fleuret.org)
(TXT) w3m dump (fleuret.org)
| sturza wrote:
| Francois, thanks for this.
| kristopolous wrote:
| See also Stanford's YouTube channel, where they post the entire
| machine learning lecture series (19 videos)
|
| https://m.youtube.com/playlist?list=PLoROMvodv4rNyWOpJg_Yh4N...
|
| They've posted a significant volume of CS lectures if you go to
| their channel. They're pretty good.
| hamzakat wrote:
| This Stanford course seems really advanced and intensive
| 93po wrote:
| I agree. Someone mentioned there weren't prereqs, I think,
| but man I was totally lost in the first class.
| barrenko wrote:
| Oh yes, there are, anyone who says there are not is fooling
| others, or assuming you're a CS grad.
| elashri wrote:
| Are you talking about the submission course or Stanford?
|
| Because There is a list of pre-reqs for the submitted
| course [1] and to be honest I feel like they are the
| standard requirements for you to fully understand DL may
| except signal processing stuff that might be taken as
| optional.
|
| [1] https://fleuret.org/dlc/#information
| xcv123 wrote:
| You will have to know the basics of calculus, linear
| algebra, and probability. A few months of study.
| PheonixPharts wrote:
| If you're interested in Deep Learning or any area of ML
| it's fairly safe to assume you have a background in linear
| algebra, probability, calculus and obviously some basic
| programming.
|
| If you're not interested in learning these areas, it's also
| safe to say you aren't _really_ interested in deep learning
| either. Which is not to say if you don 't already _know_
| these areas you aren 't interested in deep learning, but if
| you don't know them and are interested in deep learning
| you're likely already studying them.
|
| I say this because deep learning and the vast majority of
| ML really just boil down to an application of these basic
| tools. Deep learning/ML without the linear algebra,
| probability theory, calculus and coding isn't really
| anything at all.
| apwell23 wrote:
| > background in linear algebra, probability, calculus
|
| Curious. What does 'background' mean in this sentence.
| You can spend years studying just one of these in depth.
| How much is "enough" for ML?
| pedrosorio wrote:
| The basics. 1 semester course for each.
| apwell23 wrote:
| thanks! I wonder if someone has compiled a resource with
| just enough math for ML.
| patrick451 wrote:
| I'd say slightly more. Maybe it's just because I attended
| a state school, but I think my first semester calculus
| class was all single variable (20 years ago now, so my
| memory is rusty). You really to understand gradients and
| jacobians for ML, which I think was calc III for me. But
| you can skip curl and div part I guess.
| amelius wrote:
| Machine Learning is broader than Deep Learning.
|
| The Stanford course doesn't go as deep as, say, transformers.
| heyoni wrote:
| Any courses you're aware of that do?
| amelius wrote:
| Yes the posted one contains them (fleuret.org).
|
| Also recommended: https://karpathy.ai/zero-to-hero.html
| heyoni wrote:
| Oh good. Sorry I thought the implication was that this
| one fell short (as some of the other comments seem to
| suggest).
|
| Thanks!
| kristopolous wrote:
| It's actually one of the more approachable ones you'll find.
|
| Sorry but them there the facts. This stuff is hard. Otherwise
| it probably would have been done in the 1950s
| lhl wrote:
| For those interested in this course, be sure to check out his
| Little Book of Deep Learning as well!
| https://fleuret.org/francois/lbdl.html
| asicsp wrote:
| See also: Practical Deep Learning for Coders
| https://course.fast.ai/
| knicholes wrote:
| When Jeremy Howard wasn't named on the top 100 AI list, it blew
| my mind. This course is glorious.
| ultrasounder wrote:
| Instead Anthropic co-founder siblings the Amodeis made it.
| Couldnt help but notice that Marc Benioff who owns Time also
| is the Major investor(think Khosla for OpenAI) into
| Anthropic. So i would take "times 100" with a side of pickle.
| ward0 wrote:
| Another great resource is NYU's Deep Learning course by Yann
| LeCun and Alfredo Canziani that is fully available on youtube
|
| https://atcold.github.io/NYU-DLSP20/
| https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26...
| ahmedfromtunis wrote:
| Are there any good, in-depth courses that don't require watching
| videos?
| Tomte wrote:
| The handouts and slides are there. They are fully self-
| contained, not mere bullet points that the lecturer then talks
| about.
| ahmedfromtunis wrote:
| That's fabulous! Thanks for pointing that out
| lhl wrote:
| https://www.deeplearningbook.org/ , PDF version:
| https://github.com/janishar/mit-deep-learning-book-pdf
| ahmedfromtunis wrote:
| Thank you so much!
| belter wrote:
| I think this one is a bit out of date....
| CamperBob2 wrote:
| It looks like it covers the basics pretty well. Any
| pointers to alternatives?
| eginhard wrote:
| The "Understanding Deep Learning" book covers more recent
| models as well: https://udlbook.github.io/udlbook/ (free
| PDF and Jupyter notebooks available)
| barrenko wrote:
| IMVHO, GANs are entirely optional.
|
| For others, https://web.stanford.edu/~jurafsky/slp3/ will
| take you a decent way to understanding transformer
| architecture.
| lhl wrote:
| For specifically understanding transformers, this (w/ maybe
| GPT-4 by your side to unpack jargon/math) might be able to
| get you from lay-person to understanding enough to be
| dangerous pretty quickly:
| https://sebastianraschka.com/blog/2023/llm-reading-
| list.html
| Mougatine wrote:
| https://arthurdouillard.com/deepcourse/
| te_chris wrote:
| https://d2l.ai/
| villedespommes wrote:
| This is my favourite text! It really drove home a lot points
| on many architectures for me. Their math appendix is simply
| amazing as well!
| vikp wrote:
| The deep learning book is a great choice, as many have
| mentioned.
|
| I've been making a course that has a little less theory, and a
| little more application here -
| https://github.com/VikParuchuri/zero_to_gpt . Videos are all
| optional (cover the same content as the text).
| Bobaso wrote:
| I followed this course in person a few years ago. I highly
| recommend it.
| w10-1 wrote:
| I (we?) would welcome any details from you on how to assess it
| (and the many alternatives that pop up in response)?
| 93po wrote:
| I kind of want to get into this as a rusty full stack developer
| of several years, but I have no idea how feasible it is to try to
| get into this field in any capacity with 6 months of study.
| barrenko wrote:
| If you remember what derivatives are and are decent with math
| (and some probability), you have what it takes (!), and can
| pick up most of this easier than say React, in the sense that
| the incline of the ramp is much less significant, but it will
| take more time overall (so more of a marathon than a sprint).
| chasd00 wrote:
| i'll throw in this set of lectures from Andrej Karpathy. The
| first lecture is very accessible from a beginner perspective.
|
| https://karpathy.ai/zero-to-hero.html
| barrenko wrote:
| https://youtube.com/playlist?list=PLTKMiZHVd_2KJtIXOW0zFhFfB...
| another entirely approachable course (if you know some Python or
| similar language), by Sebastian Raschka.
| apwell23 wrote:
| So many options. I just started andrew ng coursera last week.
| What is the difference between all these free options.
| npalli wrote:
| These Deep Learning (and ML) courses are turning into the
| equivalent of productivity tools. Many are very high quality but
| won't turn you into a ML/DL expert. The core issue is you need to
| be spend the time to complete, learn and apply in your own
| settings. That internal drive is something beyond what these can
| do and that's the crux of the issue. People keep hoping some
| magic course will do it for them, but just like the productivity
| tools, there is no golden or silver bullet. Good old fashioned
| sit on your butt and do the work ;-).
| cinntaile wrote:
| What does this have to do with productivity tools? This is
| based on a university course that teaches you the fundamentals
| of deep learning. Of course it won't turn you into a ML/DL
| expert, that's not the point. Anyone completing this course on
| their own in their free time definitely has the internal drive
| that you say is lacking so I honestly don't get your comment at
| all.
| Kydlaw wrote:
| Lots of great resources listed here. But I think there is
| "Understanding Deep Learning" missing from the list [0]. In my
| opinion, Simon J.D. Prince accomplished a true feat with his
| book, not only through the material itself but also with the
| notes attached to each chapter, linking directly to advanced
| references (free literature review), exercises that really
| challenge your understanding of the material, and great notebooks
| with code that truly materialized the concepts learned (free
| exercises to give to students if you teach a DL class, but this
| community is probably not the targeted audience for that).
|
| [0] https://udlbook.github.io/udlbook/
| patrick451 wrote:
| I noticed on the prerequisites page
|
| > basics in signal processing (Fourier transform, wavelets).
|
| Are wavelets really basics in signal processing? We definitely
| didn't cover this my signals and systems class in EE in either
| grad school or undergrad.
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(page generated 2023-11-19 23:01 UTC)