C4W1L06 Convolutions Over Volumes

He explains this so well that I want to binge the entire playlist.

WOW, paham juga akhirnya, thanks

Why is the output 2 dimensions? If you convolve over a 2d image with a 2d filter, you get a 2d output. Wouldnt this mean if you convolve over a 3d image(R, G, B) with a 3d filter, then the output should be 3 dimensions as well right?

Edit:
I think I get it now. It's because the size of the 3rd dimension is the same for both the filter and the rgb image, so it only has to convolve over the z axis once, producing a 3rd dimension size of 1 in the output. So technically the output is 3 dimensions, it's just that the 3rd dimension is a size of 1 which is basically just 2d

If you convolved over an rgb image with a 2x2x2 filter, than the output would then be 3 dimensions.

thank u sir! You are the real hero.

Are the filter values trainable?

anda perlu menjelaskan kandungan

The way Andrew deconstructed the 3D convolution into a simple series of steps just goes in to say how great teachers can accelerate learning by manifolds.

thanks for clarifying that the filter is channel deep

Can we use different filter sizes in the multiple filter case? And what will be the output shape then?

It's funny how concepts like this can be so confusing when you don't know it. I had no idea the conv layers had an extra unconfigurable dimension and going from 3d to 2d confused me.

The most effective way of explaining depth(no of channels) of CNN

From 3×3 convolution how comes 4x4?

By far the best explanation I have ever seen. Such simple and crisp!

I had one doubt though professor, can we use CNN with data apart from images? If so, what does the filter size represent then? And how do we interpret the features of the data in terms of number of input channels?

Thank you!!!

Best explanation I've found about convolutions over multiple channels. Thanks.

thankyou sir for having great people like you in this life

!thank you so much

nice explanation

brilliant!

output of rgb channels after convolution must be 4x4x3 right?

Awesome. Hv 4 questions, scratching my head for the last 2 weeks. In my conv layer 1, I mentioned 32 filter , does that mean 32 diff features will be extracted from each image sequentially, am using greyscale image 28x28x1. Is it possible to make the filters to apply in parallel . Next, In the case of multiple filters , can the filters applied on the image in parallel or in sequential ? How to influence the conv layer to use multiple filters ? Next question is, how to override the default filter by custom filter type ?

Dude I really was searching this for 2 days but there was no clear explanation on volumes thanks a lot

ok, so at first i was a little confused by what adding all the filters at last mean. say pixal at position (0,0) for RGB are 20,10,30 after applying filter adding all the channels means [20,10,30] and not [60] . correct me if i am wrong.

Great! So a conv64 basically applies 64 different filters on segments of the input.

Is it possible that the number of filter channels greater than the number of input channels?

Found this gem after wasting my time on several 'fancy' deeplearning video tutorials.
"If you can’t explain something in simple terms, you don’t understand it."
- Feynman

@Sahil Bandar can i give ur contact no. or email id

Thank you so much! This video helped me to understand CNN very much!

I am a beginner in the field of deep learning if there is anyone who can help me in my project

Why is the RGB convolution output not a 4x4x**3** image?

Thank you , very well explained :)

a god

First Nine Numbers from red channel then 3 beneath green channel then 3 beneath blue channel? i didn't understand that aren't we taking 3x3 from each color channel?

very clear explanation thank you

Deep learning k one and only Jeetu bhaiya :)

Thanks a lot!!

You are my hero. Thank you so much

HEllo please is it possible to use 256*256*3 images for LeNet architecture .?

The formula in the summary is wrong, it should be (n x n x c) input, (f x f x c x z) filter, and (n-f+1 x n-f+1 x z) output dimensions - for z output filters and c input channels. So the convolution is a 4d tensor.

THANK YOU for ending my 4 days 9 hours search on understating CNN first layer input data structure/computation.... Moving on to the next step

Why are you stacking the features on each other? I don't get it!
normally don't we just SUM UP the features so we have only one layer of features (e.g. horizontal + vertical edges)?

Excellent. Convolution over volumes was bugging me for a long time.

Are these 3D convolutions ?

is it possible that input 1 X 1 X 155 and filter 1 X 1 X 155 for pixel classification

..so, in every 4 X 4 convoluted matrix's pixels , u put the sum of the products of the kernel pixels for the respective 3 X 3 of the imput image, for every channel (RGB)? meaning u sum the output of the dot product (kernel by respective pixels on the image) of every channel the number of one pixel in the convoluted matrix ?