15:20-->17:40Filters in CNN are managed by back propagation😊 22:30-->26:45 Shape of the filter output(Grey scale and RGB) 26:50-->28:40 Multiple filters
I watched many videos and articles but none explained clearly how this convolution operation occurs in RGB images and when multiple filters are present.Thank you so much sir!
Hello Sir, It is possible for you to make a video on all type of plots used in ML algorithms. How to draw pairplot, ggPlot, scatter, hist,kde on given dataset, which plot is best for which analysis, which points are imp to plot the plots, etc
sir every thing explained in this video is accurate, the matrix multiplication you did from 13:00 minute is wring the matrix rows of matrix-a should be multiplied with columns of matrix-b .
sir,your way of teaching is fabulous.. i didnt like ml before but now i have interest n. one question,wjhat happens if image is not square pixels like eg 28X10 like this dimension
it's just the resolution of the image. if the image had height of 100 pixels and width of 150 pixels, then the resolution would be 150*100 and if it's RGB, then we would say, it's 150*100*3.
Sir CNN me Object Detection pura deeply cover karna please , kuch bhi nii chodna usme, YOLO(all versions), RCNN, Faster RCNN, Koshis karo ki thoda jaldi le ao videos, after RNN jbb bhi padhaoge.
Why kernel size is odd ? For an odd-sized filter, all the previous layer pixels would be symmetrical around the output pixel. Without this symmetry, we will have to account for distortions across the layers. Why not even sized kernel ? It is possible to use even size kernel but you might have to suffer with aliasing error. Aliasing is distortion caused in original input. Mostly when we down sample in signal processing this is solved by low-pass filtering but this cannot be inserted in deep networks as it degrades performance.
We are not using even sized filters because while extracting important features we need to take care of surrounding pixels of an important pixel( that pixel which is a part of important component/feature of image ). If you observe even matrix 4x4 you won't find a centre pixel which surrounds all pixel but for 3x3 matrix (odd dimension matrix ) you can find centre co-ordinate of pixel surrounded by every pixel. So this is the reason why odd filters are used instead of even filters.
Early waiting for all cnn video, your work quality is far better than the paid course♥️♥️
100% right love from germany
The amount of knowledge given is this course is beyond anything. Thank you soo much making such complex topic making easy...
Can't thank you enough!!!
Bro teaches better than Andrew Ng ❤️
15:20-->17:40Filters in CNN are managed by back propagation😊
22:30-->26:45 Shape of the filter output(Grey scale and RGB)
26:50-->28:40 Multiple filters
I have learned a lot from this channel ... A great thanks for that to Nitish sir ! have a nice evening!
bhai shandar smjahya hai, mazza agya
bht acha lecture tha sir.. convolution concepts clear
Thanks a lot for helping students like us
I watched many videos and articles but none explained clearly how this convolution operation occurs in RGB images and when multiple filters are present.Thank you so much sir!
best ever explanation in the TH-cam sir, waiting for Object Detection project
The way you teach is simply amazing. Thank you for making it so interesting☺.
Best vedio on CNN
Thanks for another interesting video sir!!
Thanks sir love from Pakistan.sir you give me a great knowledge
thank you thank you ....best explanation ever
thanks sir for the video
sir crystal clear kar dia aapne to
Great explanation sir💯💯
Hello Sir, It is possible for you to make a video on all type of plots used in ML algorithms. How to draw pairplot, ggPlot, scatter, hist,kde on given dataset, which plot is best for which analysis, which points are imp to plot the plots, etc
sir every thing explained in this video is accurate, the matrix multiplication you did from 13:00 minute is wring
the matrix rows of matrix-a should be multiplied with columns of matrix-b .
That is not matrix multiplication.
Tm bahoot mst kaam krta hai Nitish bhai.
you teaches exceptionally well 🫡🫡🫡🫡🫡
cant thank you enough. this is gem
best video on cnn thank you sir
amazing maja aaya
great content🤩🤩
You nailed it...
Waiting for this only... btw first one❤
Great explanations 😦
Really helpful
Thanks for your efforts.
sir,your way of teaching is fabulous.. i didnt like ml before but now i have interest n.
one question,wjhat happens if image is not square pixels like eg 28X10 like this dimension
Best explain Sir
please explain the CNN in biomedical signal detection.
Thanks a lot Nitish
Hi Nitish,Please explain about diff filter types like Sobel etc
Best in class
Awesome Video and great content Sir ❤
best
edges primitive feat( intensity change)-> combine and find complex feat, pixel value=0-255, no of channels =3 in color image
image * filter/kernel = feature map
m*n*c image * n*n*c filter = (m-n+1) * (m-n+1) feat map of single channel
different filter on image if 10 filters => feat map (m-n+1) * (m-n+1)* 10
Excellent explanation
amazing
Sirjiii awsome explanation ❤
thanks sir
Awesome bro
100k soon !!
finished watching
excellent
25:40 is imp
from where get the number for 228*228*3 for RGB channel. for RGB we get 3 channel. but from where we get 228*228?
it's just the resolution of the image. if the image had height of 100 pixels and width of 150 pixels, then the resolution would be 150*100 and if it's RGB, then we would say, it's 150*100*3.
mxm (conv) nxn will give always give (m-n+1,m-n+1) in convolution operation
Sir CNN me Object Detection pura deeply cover karna please , kuch bhi nii chodna usme, YOLO(all versions), RCNN, Faster RCNN, Koshis karo ki thoda jaldi le ao videos, after RNN jbb bhi padhaoge.
thanks a lot sir for the whole plalist
Nitish is G.O.A.T
The best
thank you so much sir ji
255*1+255*1+255*1=765 but sir why were u still taken 255 can anyone explain???
kyuki value bhale hi 765 ari hai but maximum aapan 255 tak hi le sakte hai isiliye 765 ki jagah 255 liya
Because of intensity of grey scaled images
computer vision engineer
Nice
Sir,
Can you provides notes?
Best
Why do we always use "odd" sized filters? Interview question hai.
Why kernel size is odd ?
For an odd-sized filter, all the previous layer pixels would be symmetrical around the output pixel. Without this symmetry, we will have to account for distortions across the layers.
Why not even sized kernel ?
It is possible to use even size kernel but you might have to suffer with aliasing error.
Aliasing is distortion caused in original input. Mostly when we down sample in signal processing this is solved by low-pass filtering but this cannot be inserted in deep networks as it degrades performance.
We are not using even sized filters because while extracting important features we need to take care of surrounding pixels of an important pixel( that pixel which is a part of important component/feature of image ). If you observe even matrix 4x4 you won't find a centre pixel which surrounds all pixel but for 3x3 matrix (odd dimension matrix ) you can find centre co-ordinate of pixel surrounded by every pixel.
So this is the reason why odd filters are used instead of even filters.
❤❤❤❤❤❤❤❤
26*26
how to run CSV data on CNN??
It's 0 to 255 or 0 to 288
👍
sir please complete the playlist
Sir plz give the link of the tool you used for cnn
Hi guys it’s Sarah here 😂
So what we do? 10:23
Is the size of kernel or filter always 3x3?
No, generally
Sir sound pr kaam krin gay
I have learned a lot from this channel ... A great thanks for that to Nitish sir ! have a nice evening!