It is a discuss for individuals who know code, however who do not essentially know machine studying. Be taught the ‘new’ paradigm of machine …
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Tags: aiartificial intelligenceartificial intelligence newsartificial intelligence news 2023GoogleHeroIO19latest news about robotics technologylatest robotslatest robots 2023learningMachinemachine learningpr_pr: Google I/Opurpose: Educaterobot newsrobotics newsrobotics news 2023robotics technologies llcrobotics technologytype: Conference Talk (Full production);
It could not have been explained more simpler.
What are the prerequisites for this course? Do you have to know the sklearn framework? Or can someone who is only fluent in Python start this course?
Zero to hero implies that one is enabled to do something. That is not the case. I feel overwhelmed and underinformed now. I could have made better use of these fourtyfive minutes.
I have to say I wish I had gone through this video years ago. It's easier to play with blocks and learn what they look like before trying to learn the milling machinery to make your own blocks without. a clue of what a block looks like or why it looks that way.
even though i only understand about 15% about the video, still far better explanation than my college professor
I'm at the end of the first part of the talk and I'm speechless, it's like all the theory I've read and have been taught in class finally falls into place, I never understood how neural networks really "captured" the features, thought it was thanks to pure randomness and just some coincidence, and that the only thing that did the actual work was just minimizing the loss function and that was it, the other things were just experimenting, but after the best explanation on convolutions and pooling I've ever came into contact with now I begin to really understand why this works, so much insights, thank you so much, the first part of the talk was pure gold, the second part was good too, the amount of stuff Keras has implemented and the capabilities to expand it is pretty awesome.
Thank you
Thanks for the amazingly clear explanation!
thanks alot this video put me to sleep, day ruined!
Diabetes man.
This is beautiful.
What's her name?
here is what i dont understand
when i run training i get to 90% accuracy
but when i run the model to make a prediction
i get 25 -30% accuracy
anyone has any idea why?
Thank you
謝謝你我的超人
Thank you
for conv2d layers:
num of parameters = (unm of a filter parameters * num of input features + 1(bias) ) * num of output features.
we can consider the number of features of the first convorlutional layer is 3, which are RGB layers of a picture.
param num : 1792 = (9 * 3 + 1) * 64
param num : 36928 = (9 * 64 + 1) * 64
param num : 73856 = (9 * 64 + 1) * 128
param num : 147584 = (9 * 128 + 1) * 128
nobody mentions the typo. great presentations by both Karmel and Laurence.
Amazing!
y '(x)=0 } { y "(x)=0
❤️
Fan show ?
Absolutely amazing video!
how do i count the number of parameters by the end of each epoch?
24:06 – I'm confused with the first 2 lines.
شكرا على الترجمة إلى اللغة العربية بالسرعة االفائقة و شكرا أيضا على مشاركة هذه القناة عبر الإيميل ونحن نتابع باهتمام كبير التوضيحات والشروحات المتعلقة بأخبار الذكاء الإصطناعي وآخر التطورات في هذا المجال
How was that 512 decided
Can someone explain what a "filter" is here? I don't quite get it