23 thoughts on “Deep Learning Chapter 1 Introduction presented by Ian Goodfellow”
I thought the primary use of Neural networks is to solve non-linear functions, so why he said at 1:18:00 Deep learning models/architectures are mostly linear
It's really great! Thanks a lot!
Thank you, very good
i can see references to a book club. Is this for Ian Goodfellows's book ? If it is still active, how do I join ?
This guy really knows what he is talking about! Flawless!
Thank you Alena! Please suggest some resource for feature engineering practices used by practitioners.
Much thanks for uploading the whole series!
Auto generating spanish subtitles in the cc (the algorithm thinks Ian is speaking Spanish?). Haha is there a way for you to fix this on your end? Maybe when you uploaded the video you chose a language?
Alena, i deeploving you !!
Great video. Ive learnt more about machine learning in this video than a considerable number of others.. Hes a man that gets straight to the point with no fluff. Thanks
really cool
Ian doesn't blink much. He can't be one of us. Could be the first sentient AI. How else could one be able to teach machines to dream?
We live in a time when 30-year-old guys write bestsellers on machine learning. Amazing
Alena – Thank you!
The book consists of a plenty of ideas and hints. Practicing yourself several NN or joint some courses of practicing NN helps to understand this excellent book.
Hey Alena, Thanks for sharing this series
Привет! Спасибо большое!) Хочется побольше контента на канале 🙂
Talking about the depth of the model, in the book he explained about the another perspective of deep probabilistic model can someone explain me about that or share me some reference link.. thank you
Ian doesn't blink at all.
Thanks by the video elena The tanslation not is good
Thank you Sooo much Alena, busy going through the book, I am pretty sure these videos help me and save so much time <3
I thought the primary use of Neural networks is to solve non-linear functions, so why he said at 1:18:00 Deep learning models/architectures are mostly linear
It's really great! Thanks a lot!
Thank you, very good
i can see references to a book club. Is this for Ian Goodfellows's book ? If it is still active, how do I join ?
This guy really knows what he is talking about! Flawless!
Thank you Alena! Please suggest some resource for feature engineering practices used by practitioners.
Much thanks for uploading the whole series!
Auto generating spanish subtitles in the cc (the algorithm thinks Ian is speaking Spanish?). Haha is there a way for you to fix this on your end? Maybe when you uploaded the video you chose a language?
Alena, i deeploving you !!
Great video. Ive learnt more about machine learning in this video than a considerable number of others.. Hes a man that gets straight to the point with no fluff. Thanks
really cool
Ian doesn't blink much. He can't be one of us. Could be the first sentient AI. How else could one be able to teach machines to dream?
We live in a time when 30-year-old guys write bestsellers on machine learning. Amazing
Alena – Thank you!
The book consists of a plenty of ideas and hints. Practicing yourself several NN or joint some courses of practicing NN helps to understand this excellent book.
Hey Alena, Thanks for sharing this series
Привет! Спасибо большое!) Хочется побольше контента на канале 🙂
Talking about the depth of the model, in the book he explained about the another perspective of deep probabilistic model can someone explain me about that or share me some reference link.. thank you
Ian doesn't blink at all.
Thanks by the video elena
The tanslation not is good
Thank you Sooo much Alena, busy going through the book, I am pretty sure these videos help me and save so much time <3
Thank you for sharing the video!
Now i know why my prof. recommend me this book.