What are Convolutional Neural Networks (CNNs)?

Machine learning is truly amazing yet it pales into insignificance when compared to the ability of this chap to write backwards.

Dear lord this is perfectly chunked information.

Funny guy. Love him

Terrible explanation

perfect explanantion. I hate it when people throw difficult terms around. Why can't it be precise and clear such as using a house as an analogy. Well done!

Will the Activation Functions video come?

Wow such a comprehensive content on CNN!

Very clear and right-to-the-point explanation! Thank you!

lol u work in garage and u want teach us

so by combining the other video of yours. At the end of the the CNN there will be a discriminator which has been trained to know what a house looks like, what an apartment looks like, what a skyscraper looks like and therefore tells you that is a house ?

Awesome explanations ! ... thank you for sharing your knowledge ;))

oh my god, thankyou for the explanation. Easy to understand

This explanation is good. Thanks. 😊

This was easy to understand and very concise...Thank you

Great explanation! Great job; thanks!

Such a likeable person explaining so well, much appreciated! :)

Great video! Thanks 👍🏼

This is too low level and vague for people who need it and too high level and complicated for children, I believe that you should go more in depth to provide more information such as how the convolution works, different activation methods and different types of layers

Wait, that's a house? I thought it was the head of a tin robot.

The volume is a bit quiet here.

thanks martin for the clear explanations
you are amazing

Utterly well done, our IBM ML specialist!

What would be the difference between the standard convolutional networks and something newer like CLIP?

Explained in a very simple way that's easy to understand! Great video!

Hello, thank you for the explanation but I still don't understand how the filters are made.

Superb explaination

Application of successive Convolutional Filters well presented but at a high level only

In my eyes , the goal of Convolution is to make the signal invariant to scaling and translation. It acts as a pre-processor of the raw input signal. You could also first pre-process your training set and store it in a file. Then you can use this file and feed it directly to the deep neural network. You don't need the Convolution anymore at training.
Another way of making your signal (picture) invariant is to first Fourier Transform it to make it scaling and translation invariant. Next you transform the signal from cartesian to polar coordinates to make it rotational invariant. Finally you Fourier Transform that signal and end up with a fully invariant signal that you can store as a pre-processed Training set.

This explanation was so good. Currently using CNNs for remote sensing applications.

AWESOME! Thanks :)

this video hits different if you are currently taking digital image processing course. I feel smart lol

Can we implement this CNN to determine micro-level profiles, i.e., micrometer level?

Have been watching several videos to get a high level understanding of CNN, but no luck. However, this is a very good explanation ! Cleared lots of doubt in few minutes. Thank you

Waiting to learn more from you

👍

The intro just rocked, as to why CNN. "Humans can do object detection quickly and machines can't" and hence that's where it begins. Amazing... Thanks...

You made it easy to understand. Very helpful. Thanks a lot :)

This man rocks 🤘

Thank you..!!

Hi! Have I assumed correctly that in case of using CNNs for image recognition, the deeper the filters go, the more they zoom out on the image?
Next logical question is - what type of software is used to analyze test cases (e.g. real houses) and create those filters?

can you help me regarding my project "human pose estimation"

amazing

More please ☺️☺️

Unbelievably clear and succinct explanations

Bro this dude just wrote mirrored wth. Also thanks for the video! The concept of CNN is a lot more clear to me now. :))

clearly understandable 🙏🙏🙏

Well if the beer videos ever stop Martin you have a career in IT Vlogging 😁

Master Inventor. Cool :)

Thanks a lot!