Thank you for the great content! There is no training script in the paper's GitHub repository. Could you make a video or write an article on Medium providing training code for the Depth Anything v2 ? Thank you.
Bro, bro,, bro, read carefully Thanks A LOT FOR THIS FUCKING AWESOME CONTENTTTTTTTT! I enjoy all of your. videos, i combine your videos, with my university classes and some books and im learning at fucking scary pace, thanks for all of this videos, this helps me a lot for being a good engineer in this field. PD: do you have any book recommendation to read about computer vision ?
Thanks for such a great comment! One day I'll frame this on my wall. Super happy that the channel is helping you learn! Regarding book recommendation, I haven't read too many myself so I am probably not the best person to answer this. Books are best for understanding the foundational concepts in the field and some will also teach you implementation techniques, but they might get outdated due to recent stuff coming out. One of the better books I read that I will totally recommend is: - Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools (By Eli Stevens, Luca Antiga, Thomas Viehmann) This one covers the basics really well, and there are a lot of practical examples that will introduce you to a variety of domains in ML. Going on a tangent here... In general, when I am looking to learn a new concept (and depending on how much I already know about said concept) I do my reading from the following (non-books) sources: (a) When I want to get introductory knowledge about something: Survey papers is usually where I start my exploration - it can introduce you to a bunch of old+new stuff with the foundational knowledge you'll require to understand individual papers. (b) When I am ready to dive in more: Read seminal papers or individual papers. Maybe look for medium articles or specific lectures in video format. (c) Implement/Code help: Online articles/lectures/github/documentation etc
![The moment we stopped understanding AI [AlexNet]](http://i.ytimg.com/vi/UZDiGooFs54/mqdefault.jpg)
Thanks for your great video. I also enjoyed reading your MDE article on medium!
Thank you for the great content! There is no training script in the paper's GitHub repository. Could you make a video or write an article on Medium providing training code for the Depth Anything v2 ? Thank you.
Love your videos as always!
Great piece, thanks for the effort you put into it.
Concise and so well put!
Thanks for sharing!
Great work
Wonderful
Great video!
Bro, bro,, bro, read carefully
Thanks A LOT FOR THIS FUCKING AWESOME CONTENTTTTTTTT!
I enjoy all of your. videos, i combine your videos, with my university classes and some books and im learning at fucking scary pace, thanks for all of this videos, this helps me a lot for being a good engineer in this field. PD: do you have any book recommendation to read about computer vision ?
Thanks for such a great comment! One day I'll frame this on my wall. Super happy that the channel is helping you learn!
Regarding book recommendation, I haven't read too many myself so I am probably not the best person to answer this. Books are best for understanding the foundational concepts in the field and some will also teach you implementation techniques, but they might get outdated due to recent stuff coming out. One of the better books I read that I will totally recommend is:
- Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools (By Eli Stevens, Luca Antiga, Thomas Viehmann)
This one covers the basics really well, and there are a lot of practical examples that will introduce you to a variety of domains in ML.
Going on a tangent here... In general, when I am looking to learn a new concept (and depending on how much I already know about said concept) I do my reading from the following (non-books) sources:
(a) When I want to get introductory knowledge about something: Survey papers is usually where I start my exploration - it can introduce you to a bunch of old+new stuff with the foundational knowledge you'll require to understand individual papers.
(b) When I am ready to dive in more: Read seminal papers or individual papers. Maybe look for medium articles or specific lectures in video format.
(c) Implement/Code help: Online articles/lectures/github/documentation etc