How convolutional neural networks work, in depth

Very good tutorial. Learn so many things

If I want yes/no answer say if there is a puppy in the 500x500 image - and I have a bunch of images with puppies - and I want to train CNN from scratch on those - how do I know what features of the puppy are or how many should there be? Like in X and O example you knew the “/“ and “\” features beforehand but what if you want for example to classify a cactus in a desert and it’s “feature” is “v” like a vertical line and a shade from the sun on the ground or something…what’s the intuition to tune the features for the neural network to perform the best for a specific task at hand?

Don't let the duration of this video intimidate you from enjoying this masterpiece of a presentation, just press play and begin, you'll freaking love every second of it.
Thank you so much for sharing this and so much other information for free!

I am a visual learner with no background of computer science and this video is a gem! Thank you very much. Subscribed:)

You are simply the best at explaining this complex topic. Thank you.

Stop waiting, start playing = working

I'm only halfway through but really, you're amazing at teaching and explaining concepts. Thank you

This is what a tutorial video should be!

Your explanation is amazing, from your video i can understand neural network. Thanks

I'm so glad to finally find the videos about NN explained by somebody whose English I can understand.

This is by far the best video I've seen on CNN. Thanks a lot!

Thanks, but you didn't teach the convolution part 😥

I can't stress enough how great your videos and explanations are. I get overwhelmed by lots of text and missing visual examples, so it's great I found your videos. Watched 2 already and will definitely watch the rest too!

A one hour well spent.,,in my Life...

Amazing!

Thank to the author. Does anybody get from this video how all this works with shift/rotation/scale of the image?

Sounds like Howard Hamlin from Better Call Saul

Thanks for your great video! There is something that is confusing to me, can someone clarify this: Why there is an average pooling between the convolution operation and the feature mapping in the convolutional layer? In other words, why is the feature map the result of an average pooling in your convolutional layer? I thought the feature map was generated by summing the products between the filter weights and the corresponding input values. No?

A remarkably intuitive video for beginners. Thank you

Very clearly spoken and illustrated. It's great to have well articulate and easy to follow tutorials like this.

I can't imagine how hard it was to make this cool video! Many thanks to the author!

one of the best videos about this topic I have ever watched. It is 1 in a thousand! Thank you for sharing it

Man, thank you so much!
This is incredible work!

One of the best videos I’ve ever seen on the topic: super clear explanation + truly in depth, all without being boring. The only thing I didn’t understand is how to determine the values in the matrices for the convolution.

this reminds me of stewie sayin cool whip

Thank you so much! I didn't have to pause once to understand anything. You explained it so perfectly.

It's an excellent video

thank you so much for your explanation. really helps me to understand what CNN is about

This is the video I needed the most. Thank you

Thank you for this amazing video! It definitely helped clear a lot of stuff about CNNs for me. On a very random note, you have a great voice! I feel like you'd make an awesome audiobook narrator!

awesome perfect stuff. if you could have also explained batch training and stochastic gradient detail a little bit more, it would be more perfect.

Really good explanations. Just the right level of detail for my understanding. Thanks.

thanks good explenation

Fantastic video. The conclusion really summed up everything nicely.

Hey what are the prerequisites one need for watching and comprehending this video?

You are a great teacher.

Master class! thank you!

i loved your detailed explanation of the steps, but can you please make another video to explain the REASON for each of the steps in detail?

amazing lecture

Likely filtering looks to be rather precised so is there smth wrong on other layers? Or at the point of gathering filtred image it has an issue?

Why actually neural network has a trouble in recognizing well precised boundaries of image details? Somehow it works but obviously a quality of recognition is quite poor

Nice tutorial.
Do you use any specific plaftorm such as Keras or Pytorch.
I've seen some tutorials and examples using a convolutional layer like this
Conv2D(filters=32)
Which is supposed to tell Keras to use 32 convolutional filters.
But it doesn't specifies what filters to use, it seems to be something automatic.
How does Keras compute that 32 filters? What filters is it really using? (I know horizontal, vertical, vertical, cross, sobel...)

Hello :) Thank you for this presentation is really really useful. If swapping my data makes no different what would be a nice model to use?

Top notch!

Best explanation of how Neural Networks work I have watched so far! Well explained and really intuitive