Great explanation of ML.
Great info, thank you for sharing!
Arguments is what you pass into a function not parameters, just saying.
Thank you for the great and simple explanation. I need one clarification when you were talking about how convolution can help in compressing the image @ 18:40. You mentioned reducing the number of pixels from 150 to 148 and explained the reason; however, in the same slide what is the number 64 (first row, under Output Shape) referring to and how does it affect the calculation and the performance?
Wow , Thanks Laurence Moroney for making 'Convolutional layer' no more convoluted for me ….also you have made the concept of 'Pooling' so clear .
Anyone knows font name from the presentation?
where I can get datasets for my tensorflow. For example, in tensorflow.org they have given the Fashion MNIST dataset by which if I give this in my jupyter notebooks it automatically downloads. Like this is there any other datasets? Please help me sir.
I would recommend this video over most others related to machine learning. Good form guys keep up the good work.// congratulations on the new belt!
This is an amazing smooth intro to a relatively complicated topic. The QR codes are a smart move. Hoping to see more videos on similar topics. Thank you both.
Is anyone else bothered that he used (1.5 * 42) instead of (1.5 * 142) for the convolution at 12:20 or am I just an OCD weirdo?
why do we need skin tones for rock, paper and scissors?Also Why do we need to find out male or female or children's hands?All computer needs to know is how rock, paper and scissors gonna look like with fingers. Looks like the engineer (read as Google) is in mind set to collect as much info as possible. or include "diversity and inclusion" in all their speeches. BS
I just learnt basics of Python to gradually start delving deeper into machine learning. This awesome explanation has given me a boost. Thanks Laurence !
Wow. Thank you so much for this!
18:10 top-right corner :3
Laurence's explanations were wonderful! Thorough, but also simple enough that even a newbie can understand it. Thank you!
Nice presentation. Pretty awesome experience.
Very nice talk for people who are beginning to get into machine learning. Thank you for the great explanations!
Wow I've been reading about this stuff a lot but always had difficulty wrapping my head around it. I did the tensorflow demos, but was unable to understand it well enough to try something on my own. He explained it very clearly and cleaned up a lot of my misunderstanding. Thank you so much for sharing!!!
Nice explanation. Thoroughly enjoyed the presentation 🙂
I have a question. I've been studying deep learning for about a few weeks myself and have little knowledge of python mainly just beginner level. Is there a step by step on how to use the code that was provided for further study?
yep. Zero to Hero, i've recently made something with coral dev board( order from a online store: store.gravitylink.com/global) that could accelerate ML processing, it really surprised me.
Best book of deeplearning?
How did he come up with those filters which capture vertical lines and horizontal lines.I understand deep learning is like a black box and most of things are learnt.How did he debug and found the filters which does the line detection?
"Laurence moroney" never forget this name . His enlighten wisdom really goes straight through my head .
Was lucky to see Laurence in person when he did a presentation at my school. Thanks Laurence for an interesting and informative presentation! Got me interested in ML
Very useful introduction of ML, thanks a lot. Beginner here, where could I find the code of the demo so that I can study it in colab in order to understand ML more?
Best presentation 🙂
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