Tutorial 22- Padding in Convolutional Neural Network

thanks Sir

Thank you man clear and simple as it should be

🤩🤩🤩🤩🤩

Strides explanation?

A small question : Are filters always a square matrix or their dimensions could be adjusted by user to their use ?

Great Effort to make the things so simple. Dedicated teacher. Keep going...

sir why should we take always filter is 3x3

for a given input and output , can we find the corresponding filter

Are the values inside the filter always the same , and how are they computed?

Good teaching 🤠

Great Bro...! But i think Binary value '0' means Black & '1' means White. is it right sir?...

Thank you, Krish sir. Nice concept. Beautifully explained.

Can we use 2*2 filter

In machine learning, there ANN. Also in deep learning, namely in CNN there also a classifier ANN. How can we pass numeric data (.csv file) as an input to a convolutional neural network?

Worthless without code

Thanks Krish

You might got 10 dislike mistakenly bcoz while watching this video I have unintentionally clicked the dislike button but while upvoting it I get to know and rectified my mistake 😅

Easy and to the point 👍

Missing denominator as stride in the formula because your are taking stride one bit what if stride is two

thankyou sir..

Awesome video! Very explanatory, thanks.

Excellent

Awesome sir

Great video. can you please let me know what mike you are using?

You forgot to mention that the strides may also have an effect. Your example is true if the stride is 1.If the stride was to be 2 for instance, things would change. Use the formula;

Dimensions = [(input image size-Filter size+ 2*Padding size)/stride size + 1].



In your last example with the stride of 1 and padding of 1 as you only have one layer of 0's surrounding your original image,the solution is worked out as follows:

Dimensions = [ (8-3) + 2 * 1)/1 + 1]

= 8

Therefore the feature map has dimensions of 8 * 8

Thank you so much sir for this easiest explanation.

Awesome 💞

Thank you Sir

Expecting videos on 3D CNN as well ...

Thank you sir

Very well explained! Kudos Krish!

in 6×6 matrix if stride =2 and padding=0 what will happen

great video.Please provide the ipynb notebook link wherever necessary.Thanks.

heang natuuuuuu

Sir , one ques: when we can use convolution so it's okay to be if we've lower pixel size compare with input , losing data and padding could see it's necessary to make sure not loosing any data.

Sir please add RNN lectures also

Next video on Recurrent NN , GANs,Autoencoders , Natural language processing please

Please upload more videos in this playlist :) eagerly waiting

Hi Krish Sir, I really like your teaching style.... you have made clear many many doubts of mine. Thank you very much. And Sir I have a request, kindly upload the further videos on this series as I have got exams recently. God bless you .

When are you uploading a new video on deep learning ????

Hi Krish....cud u continue sessions on GANs,Variational Autoencoders,CNN and its various architectures and applications in img processing like Unet for semantic segmentation......

Please give videos of practical implementation also.

please upload more videos on deep learning.....waiting for more videos

hey krish do upload lectures on RNN,Autoencoders , boltzmann , belief networks and GANs too with implementation

Krish,
Please do more videos on CNN,RNN .Waiting for it

sir please upload the videos regarding recurrent neural network

Hello Sir..! can you upload a practical example that how to build an Image Classification Model in deep learning??

krish kindly do video'S on RNN ...very much appreciated for your effort