Well done Laurence. Your work is inspiring and educative. You are an amazing tutor. I have a big challenge with my Ph.D thesis as it is related with Optimizing Routes using ML and IoT in Waste Management. Is there anyway I can contact you to discuss with you? Via email?
What do you mean by convolution? I learned it was a mathematical term equivalent to shifting one function along an axis, integrating, and presenting the result of that integration as the 'convolution'. We performed convolution by hand. Then we Fourier transformed the two functions, multiplied them together, Fourier transformed the result back into a real function, and realized that was the same as convolution. It appears you have a different definition.
i think it's a little bit irritating. you have oldschool imageprocessing-convolution which he presents and you can use as preprocessing for a Deep-Learning-MLP. But there are also Neuronal-Networks (CNNs) which are doing similar things by design of the network, and that is more complicated to explain.
I can't even tell you how much more I enjoy using TensorFlow 2.0 than I did using the previous versions. Thank you for all the great improvements. A special thank you for TensorBoard. A very useful and fun tool.
@@LaurenceMoroneyThank you for the incredible presentation Mr. Moroney. It took you just 2 hours you said. I assume it's a similar story as the guy that once charged his client 1000 dollars for a 10minutes service. The client complaint about why he pays so much for only 10 minutes and the guy answered it took him 20 years of experience to do that in 10 minutes.
I just came here because I today saw the Colaboratorya and then started watching the video. Know it's really helpful for me to understand how easy is TensorFlow and I'm on the right path to learn the all. Thanks to RUclips and I/O.
I just love Laurence Moroneys presentation style. Outstanding ability to read his audience and adjust on the fly in tonality & emphasis. Awesome skill & great presentation
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.
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!!!
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.
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
Finally, I got this shit. I have been watching various speeches and talks for the past two years and every time I thought I got it, I didn't. Of course, until I stumbled upon this video. Now I know the concept enough that I can explain it to both technical and none technical people. Thanks for the wonderful video.
I have zero experience in Machine Learning, but with this explanation, I feel like I can make a rock paper scissors game with just a little more knowledge.
This is how a quality teaching and explanation can help everyone to explore in depth rather than the traditional teaching and old school explanation does...
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 .. this was an awesome presentation, I'll focus on the first speaker since I was somewhat familiar with the subject (I also liked the second one). He explained all of the key concepts as clearly as I have seen - and I have looked at a LOT of videos, this is a master class in how to do it. I hope he presents on many more related topics. Definitely worth watching. Thank you so much!
I've been waiting and waiting thinking the only way to properly learn machine learning is through university courses. I've been wrong. I appreciate the work you guys are doing to motivate the next generation of programmers to understand that machine learning is something anyone can understand and use as long as you've put the effort in. Thanks guys :)
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
I hope i dont ask a stupid question, but why are F512 at 6:42? I understand what pixels are. I understand the size of 150x150 pixels and color dept of 3. But why 512 functions? is this a calculation or a random number? Sorry for my bad english, but english is not my native english.
I remember when Laurence taught in Coursera "Tensorflow in practice" course how to recognize that same rock, paper and scissors images, and in the way that he explained it was pretty easy to implement. Excellent teacher!
Respected ma’am, I am Polaka Divya Reddy from India, I came across your profile while applying for MITACS internship on Edge Devices. Your work is truly inspiring!! I was really intrigued by your work on software testing and federated learning. Your work on systematic mapping was very comprehensive and went in depth! Comparing the quality of automated test scripts has always been a challenge. In federated learning, I do feel that there is a need for better development tools so that we can build these FL systems easily, especially for application level support (APIs). I wanted to know if you faced any efficiency issue in edge FL due to computational power of edge nodes? Also, THANK YOU SO MUCH for these videos, it helped me revise concepts better. I really hope to work with you next year!
Your explanation is really cool. Learned a lot of things in this 30 minutes of video. You explained every thing that required to quick start with machine learning. I would suggest beginner to start learning from this video. Thanks a lot.
Great video Laurence! So much information condensed in such a short time. I know what happened here, you wrote drown the script of your talk and then used convolutional layers and max pooling to compress it 😂 . By the way, Karmel, you did awesome too explaining all the deployment options.
Thank you very much for this outstanding presentation. Congratulations for your teaching style. Finally, I can understand better those difficult concepts that are presented in such clear simple and clever way.
Thanks Laurence and Karmel for this great presentation. I am having my medical and vision science research in AI, and as a starter in computer science , I have been learning and watching a lot of videos aside my Supervisor's recommendation for the past weeks. In fact, this is great and well articulated making me easily grasp all that I have learnt. God bless you and the team. Can we look at creating more deep learning partnership practice and real solutions in Africa! Thanks - Michael @ PHG Foundation Projects Hub - Ghana/Africa !
Nice explanations and tools. I think mainly why I am struggling to make use of machine learning is because I am attempting to implement it from scratch.
I am making my own language which can combine all the language which had been made till now, .... This video is bit helpful for me to add some magic in it 😃😃
I love the idea, but installation using Anaconda with Windows makes Spyder and/or Anaconda prompt unresponsive. This problem also occurs when using Keras because TensorFlow is the back-end. I think I've got a working set up now but this was after 2 un-install and re-install attempts.
Around 8:30, where does he provide the correct answers for the training data and relate these to the 3 output neurons? Also, the diagram shows three output neurons fed by the first neuron/function in the layer above, aren't they fed by all 512 neurons?
Try the workbook here for yourself: github.com/lmoroney/io19/tree/master/Zero%20to%20Hero The labelled training data are the correct answers. So by training, you are telling the network 'this is what rock looks like' etc. It then tries different weights/biases in each of the functions (neurons) until it gets a set that is accurate at guessing the correct answer.
@@laurencemoroney655 , by the way I love your videos and would highly appreciate if you could make one about recommendation systems based on matrix factorization, in perticular there practical implementation.
![Noob To Max With DRAGON REWORK In Blox Fruits [FULL MOVIE]](http://i.ytimg.com/vi/LnBMOinoOvA/mqdefault.jpg)
I have been studying Deep Learning for the last 3 weeks or so and this guy explained it like in 20 minutes, I wish I had a teacher like him :(
Thanks Akshay! I'm teaching on Coursera if that's any use :)
try siraj? he uses the same methods
@@LaurenceMoroney we are super thankful for your understandable and great explanation 🙏
oh I absolutely agree. There's another fellow on the interwebs that explains it in a similar way but this one is by far the best
@@BrianThomas Thanks! :)
And that’s what an explanation is called.
Wow the way he explained I grasped each and every word of his.
Thanks though
Wow, thanks Raaghav!
Well done Laurence. Your work is inspiring and educative. You are an amazing tutor. I have a big challenge with my Ph.D thesis as it is related with Optimizing Routes using ML and IoT in Waste Management. Is there anyway I can contact you to discuss with you? Via email?
The best explanation of convolution in few minutes.
🙏
Thanks, Asif!
Shanti bro.
What do you mean by convolution?
I learned it was a mathematical term equivalent to shifting one function along an axis, integrating, and presenting the result of that integration as the 'convolution'. We performed convolution by hand. Then we Fourier transformed the two functions, multiplied them together, Fourier transformed the result back into a real function, and realized that was the same as convolution. It appears you have a different definition.
i think it's a little bit irritating. you have oldschool imageprocessing-convolution which he presents and you can use as preprocessing for a Deep-Learning-MLP. But there are also Neuronal-Networks (CNNs) which are doing similar things by design of the network, and that is more complicated to explain.
This guy is sssssooooo highly skilled in explaining his topic - its just amazing.
Thank you for that.
Thanks!
There should be a button of "Mega Like" for this video. Great explanation of an epic tool. Thanks a lot!
Oh thank you so much! :)
@@laurencemoroney655 thhb4uihh i
Banii=mony ? Cat cost price fel fe recjama say indemnn de a access inainte say apjicatie sa for explcata mai intai cat Costa!!..
@@laurencemoroney655 Great presentation!
I can't even tell you how much more I enjoy using TensorFlow 2.0 than I did using the previous versions. Thank you for all the great improvements. A special thank you for TensorBoard. A very useful and fun tool.
Thanks, Bianca!
It takes a lot of hard thinking to make a topic like this appear this simple.
The Feynman method
Haha. I came up with this talk in about 2 hours in a coffee shop in Tokyo. Maybe their coffee is really really good! :)
@@LaurenceMoroneyThank you for the incredible presentation Mr. Moroney.
It took you just 2 hours you said. I assume it's a similar story as the guy that once charged his client 1000 dollars for a 10minutes service. The client complaint about why he pays so much for only 10 minutes and the guy answered it took him 20 years of experience to do that in 10 minutes.
Wow , Thanks Laurence Moroney for making 'Convolutional layer' no more convoluted for me ….also you have made the concept of 'Pooling' so clear .
I just came here because I today saw the Colaboratorya and then started watching the video. Know it's really helpful for me to understand how easy is TensorFlow and I'm on the right path to learn the all. Thanks to RUclips and I/O.
Welcome!
I just love Laurence Moroneys presentation style. Outstanding ability to read his audience and adjust on the fly in tonality & emphasis. Awesome skill & great presentation
Thanks Dino! :)
Not everyone who knows something is good at explaining it... this lecturer is fantastic!
Thanks, Angelo!
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.
Thanks, Eddie!
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!!!
That's great to hear! Thanks! :)
This is the best video I have seen to demystify TensoFlow, convolution and pooling. Thanks Laurence!
Welcome!
I did the Introduction to Tensorflow course a couple of months ago on Coursera. I got all that revised in 35 mins. Thanks for this great video.
Welcome!
This is one of the best video on machine learning
Thank you, Balaji!
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.
You're welcome! Glad you enjoyed! :)
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
Laurence's explanations were wonderful! Thorough, but also simple enough that even a newbie can understand it. Thank you!
Welcome! Glad you enjoyed :)
Finally, I got this shit. I have been watching various speeches and talks for the past two years and every time I thought I got it, I didn't. Of course, until I stumbled upon this video. Now I know the concept enough that I can explain it to both technical and none technical people. Thanks for the wonderful video.
Welcome. But it aint $#!+
I cannot believe how understandable you made such a dense topic (mostly) easy to conceptualize! This was wicked! Nicely done!
Thanks, Alex!
@@LaurenceMoroney other way around! I can't wait to go through the rest of the catalog!:)
I have zero experience in Machine Learning, but with this explanation, I feel like I can make a rock paper scissors game with just a little more knowledge.
Awesome -- you can do it! :)
This is how a quality teaching and explanation can help everyone to explore in depth rather than the traditional teaching and old school explanation does...
Thanks!
Wow. 35 min and I learned so much about tensorflow !!
Nice! Glad it was useful for you! :)
www.coursera.org/learn/introduction-tensorflow/home/welcome
Thank me Later
I just learnt basics of Python to gradually start delving deeper into machine learning. This awesome explanation has given me a boost. Thanks Laurence !
Great to hear, thanks Yash! :)
Hey harsh were u learn basic of python plz share
This is the best tutorial/introduction that I have ever watched
Oh wow! Thank you :)
Machine learning Zero to Hero what a captivating title
Thanks Marco! :)
And somebody please give this man a hand of applause!
But is that hand a rock, a paper or a scissors? :)
@@laurencemoroney655 I maid this comment related to avengers dialogue " And somebody please give this man a shield".
@@ritik84629 Haha! Thanks :)
(Aside from the talk being great) The presentation screen is awesomely beautiful!! Imagine seeing this for the first time, even just from year 2000.
I know! I had to keep turning around to look at it. Even up close it was beautiful! :)
I need to see it too for real! ^ - ^
Best video I have ever seen online to understand deep learning 👏🏼👏🏼
Wow...thanks! :)
I got started with machine learning today. Your videos are so good that even an absolute beginner like me could understand it
Glad you enjoyed! :)
This is the best artificial intelligence video available. Great explanation. Thank you Google Team.
Thanks! :)
Wow .. this was an awesome presentation, I'll focus on the first speaker since I was somewhat familiar with the subject (I also liked the second one).
He explained all of the key concepts as clearly as I have seen - and I have looked at a LOT of videos, this is a master class in how to do it. I hope he presents on many more related topics. Definitely worth watching.
Thank you so much!
THanks esmail! :)
I've been waiting and waiting thinking the only way to properly learn machine learning is through university courses. I've been wrong. I appreciate the work you guys are doing to motivate the next generation of programmers to understand that machine learning is something anyone can understand and use as long as you've put the effort in. Thanks guys :)
Welcome! :)
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
Thanks Tm! Which school was this at?
It was at UH Manoa
@@tmnic6971 Ah yes! What a great night that was! Hope to go back soon :)
I hope i dont ask a stupid question, but why are F512 at 6:42? I understand what pixels are. I understand the size of 150x150 pixels and color dept of 3. But why 512 functions? is this a calculation or a random number? Sorry for my bad english, but english is not my native english.
Purely arbritrary. You could try different values to see if they work better.
its simply awesome, i been using jupyter note book, and it has cleared my many queries running in my head, and lot more still remaining,. Good work.
I desperately needed an overview like this, thank you!
Welcome!
is this the crispiest hd quality video on youtube? Excellent content too
You can choose video quality up to 1080p 60
what a man !! i am a surgeon and hardly know how to open the computer and i understand what he did say !!
Oh wow! Thanks Waleed!
I remember when Laurence taught in Coursera "Tensorflow in practice" course how to recognize that same rock, paper and scissors images, and in the way that he explained it was pretty easy to implement. Excellent teacher!
Thanks Sebastian! :)
The best content for the Zero people. 👍
You're never a zero person :)
I might have been watched this 1 million times this week :D
A-ha! So that's where all the views are coming from! :)
@@LaurenceMoroney you're sense of humor is commendable.
The best explanation of convolution in few minutes.
🙏
This is amazing!!!!
Thanks! :)
Nice explanation. Thoroughly enjoyed the presentation :)
Stuff like this makes me so happy I'm a cs major
I was a Physics major :)
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!
Thanks. But what belt? :)
Respected ma’am, I am Polaka Divya Reddy from India, I came across your profile while applying for MITACS internship on Edge Devices. Your work is truly inspiring!! I was really intrigued by your work on software testing and federated learning.
Your work on systematic mapping was very comprehensive and went in depth! Comparing the quality of automated test scripts has always been a challenge. In federated learning, I do feel that there is a need for better development tools so that we can build these FL systems easily, especially for application level support (APIs). I wanted to know if you faced any efficiency issue in edge FL due to computational power of edge nodes? Also, THANK YOU SO MUCH for these videos, it helped me revise concepts better. I really hope to work with you next year!
24:06 - I'm confused with the first 2 lines.
Your explanation is really cool. Learned a lot of things in this 30 minutes of video. You explained every thing that required to quick start with machine learning. I would suggest beginner to start learning from this video. Thanks a lot.
You're welcome! Thanks for your feedback :)
No words to congratulate him, what a good teacher, and i saw this video at the right time.
Great! Glad you liked! :)
Great video Laurence! So much information condensed in such a short time. I know what happened here, you wrote drown the script of your talk and then used convolutional layers and max pooling to compress it 😂 . By the way, Karmel, you did awesome too explaining all the deployment options.
Haha! The secret is out! :)
Laurence Moroney was
This is amazing!!!!
Machine learning Zero to Hero what a captivating title
Thanks, Lindsey! :)
This is the best explanation on the internet, Thank you so much for this classy Talk.
Oh wow!! Thank you so much! :)
Thank you very much for this outstanding presentation. Congratulations for your teaching style. Finally, I can understand better those difficult concepts that are presented in such clear simple and clever way.
Awesome, thanks for the feedback!
Thanks Laurence and Karmel for this great presentation. I am having my medical and vision science research in AI, and as a starter in computer science , I have been learning and watching a lot of videos aside my Supervisor's recommendation for the past weeks. In fact, this is great and well articulated making me easily grasp all that I have learnt. God bless you and the team. Can we look at creating more deep learning partnership practice and real solutions in Africa! Thanks - Michael @ PHG Foundation Projects Hub - Ghana/Africa !
THank you! :)
I wish I could I attended it live :(, but this video is the best explanation of convolution you'll ever see!
We were the morning after the party the night before! Many people couldn't make it!
Simply the best !
Superbly complements Moroney’s book : AI and Machine Learning for Coders
The feedback from this talk inspired it :)
Super presentation! Nice explanation on concepts in Machine Learning using TensorFlow, 'Convolutional layer,' and 'Pooling'.
Thanks Ravji!
Nice explanations and tools. I think mainly why I am struggling to make use of machine learning is because I am attempting to implement it from scratch.
Good to start with high level stuff like this, and as you get more familiar, you can peel away the layers to optimize
Laurence always killing it! :)
Thanks!
Very nice talk for people who are beginning to get into machine learning. Thank you for the great explanations!
Welcome! :)
Very fast and clear talk! Love it! Thanks a lot for sharing!
Glad you enjoyed! :)
This is one of the best video on machine learning
This is amazing!!!!
This is amazing!!!!
This is great. I wanna start something in ML. Thanks for sharing
Nice :)
Great presentation! Loved how he explained the concept so clearly. Definitely going to try that codelab.
Thanks, Vivek!
I am making my own language which can combine all the language which had been made till now, .... This video is bit helpful for me to add some magic in it 😃😃
This is phenomenal.
I have never seen an explanation like this before.
Thanks!
great explanation mr Laurence... simplified explanation for complex things ... I have better understanding listen you. thank you
THanks! :)
That's the level of simplification we need...🙌🏻 thanks Laurence
:)
I love the idea, but installation using Anaconda with Windows makes Spyder and/or Anaconda prompt unresponsive. This problem also occurs when using Keras because TensorFlow is the back-end. I think I've got a working set up now but this was after 2 un-install and re-install attempts.
IKR, I spent days trying to set Anaconda up, until I discovered my savior Colab.
I've used the virtual environments in PyCharm. They work great, even on Windows.
The best explanation of convolution in few minutes.
🙏
this is good
Thanks!
"Laurence moroney" never forget this name . His enlighten wisdom really goes straight through my head .
Wow...thanks Vipul
This man is saviour 👍🙏
You've never seen my play as goalkeeper. Trust me, I'm not very good at saving.
Ha ha ha... u have a nice sense of humor too 😁😁
even though i only understand about 15% about the video, still far better explanation than my college professor
LOL. Hopefully I can help get closer than 15% :)
This is the best video that explains everything in detail. Thank you for this.
Thanks!
Nice presentation Laurence
Mitch! How you doing? :)
Amazing video, there's many elements here that are explained so well. Looking forward to doing more stuff with TF in the future! :)
Thanks! Best of luck with it!
I keep rewatching this vid, it's just so good
Thank you! :)
how do i count the number of parameters by the end of each epoch?
Finally they came down from Mount Olympus to share the knowledge of the gods 🏔
Ha! Not really. We're just normal people like you! :)
Wow I wish the campuses taught like these people... Machine Learning is the Future
Thanks!
Around 8:30, where does he provide the correct answers for the training data and relate these to the 3 output neurons? Also, the diagram shows three output neurons fed by the first neuron/function in the layer above, aren't they fed by all 512 neurons?
Try the workbook here for yourself: github.com/lmoroney/io19/tree/master/Zero%20to%20Hero
The labelled training data are the correct answers. So by training, you are telling the network 'this is what rock looks like' etc. It then tries different weights/biases in each of the functions (neurons) until it gets a set that is accurate at guessing the correct answer.
I love the way they talk. Excellent, I learned exactly what I was looking for))
That's great, thanks Robert!
@@laurencemoroney655 , by the way I love your videos and would highly appreciate if you could make one about recommendation systems based on matrix factorization, in perticular there practical implementation.
@@robertalaverdyan3150 Oh man. I'd need to learn that first :)
Nice explanation sir looking forward for more such sessions from you...
Thanks! :)
Absolutely amazing video!
wow this is just amazing. This is the best explanation of deep learning. Thanks much.
Thanks!
Thank you for the useful video. I am curious about deep learning and your explanations showed me a lot of useful insights.
Thanks, Devin!
Whoa, thanks for this video! I learned a lot about making a Model, more about layer, convolutions, pooling for compression, input size
Thanks! :D
Welcome! :)
His way to explain is impressive....
Thanks!
AMAZING presentation!
Thanks!
Laurence Moroney presentation skills are amazing. this talk feels like a one sided conversation.
Thanks Gabriel! :)
8:58 , crystal-clear explanation of "Neural Network".
Thanks! It's an approximation to help clarify the concept. Hope it helps :)
19:15 how do you and where, if you do tell it to move one down and one right ?
The Conv2D does that automatically. It turns the 150x150 into 148x148 as a result
Nice explanation Laurence Moroney and Karmel Allison, can you please share the .ipynb file link.
Thanks.
QR Code with link is in the slides -- or you can get it here: github.com/lmoroney/io19/tree/master/Zero%20to%20Hero
colab.research.google.com/github/lmoroney/io19/blob/master/Zero%20to%20Hero/Rock-Paper-Scissors.ipynb
AI is conquering the world. I love this! Moroney and Karmel, thanks for the great effort!
Welcome!
So simply explained, this is amazing, thank you
Thank you! :)
Great explanation of ML.
Thanks Indy!
Really wicked of you Mr Laurence Moroney. Thanks for teaching!
You're welcome, Atal!
Thanks google io for publishing mechine learning tutorial.
Welcome!