hello my name is Krishna and welcome to my youtube channel now this is my 23rd tutorial on the complete deep learning playlist I know guys like this was like it I had actually skipped many days in order to upload this particular tutorial there were some problems I went I had to actually make up this particular setup and the previous setup was a little bit rude so now I've made this setup properly I'm extremely sorry for the delay now I'll be continuously uploading videos every with respect to deep learning and will try to complete within two weeks so today in this particular video we'll be discussing about the operations of CN n that is convolution neural network and we try to see the basic differences between an artificial neural network operation and a convolution neural network operation so in this left hand side this is basically my n and operation and in the right hand side and basically draw my CN n operation and we will try to discuss this we'll try to find out the basic difference then how the operation usually takes place in this case how the weights are getting updated that I have already made a video regarding that in my previous videos in previous in in my past twenty second and twenty first tutorial I have actually created a video on padding and actually shown you what is convolution operation so let us go ahead and try to see because after convolution at operation the filters that we are choosing in convolutional neural network how this is getting updated will try to understand that and one more thing guys if you are looking for some suggestion towards the transition of data science or if you want some advice with related to the real life data scientist I wish I'll be sharing some information so make sure that you watch this particular video till the end so let us go ahead now over here let me just consider an artificial neural network and suppose this is are my inputs X 1 X 2 X 3 and these are my weights that gets assigned W 1 W 2 W 3 you know if you have not seen this videos you can completely go and check my playlist again the link will be given in the description now we know that in the first iteration I did but when we go to the first hidden neuron in the first layer we will multiply all the weights and the inputs right and I am assigning it as dead and after this we will also add a bias after adding bias we apply a raloo or some activation functions like sigmoid relu and and we have already discussed about this now similarly if I take the same operation with respect to a CNN you know that in CNN we have some image it may be of any size like it may be current in this particular example I have taken for cross flow now when I take this image I can initialize some filters you know filters or corners these are called as kernels now why this filter is basically used it is basically used to you know to find out the whole horizontal edge detection vertical edge detection many things like that right and we have also discussed about this now the convolution operation basically says that okay we have understood that how the correlation operation takes place we will take this value suppose over here I have some values like this 5 6 7 8 9 10 11 - and these are our pixels right if you know and if I'm considering this as a you know great pixel white and black images I'll have one pixel value right if I consider this as RGB image then it will have three channels one is our channel G Channel and B Channel and each and every field will be represented with the help of three values you know the R value suppose this R value is 128 this G value is something like 100 this blue value is basically like car 250 right so when this all the colors have combined we basically get a color image but in the case of grayscale image will just have 1 pixel values now what will happen suppose this is my this is my filter okay so suppose this is my values of the filter and consider that this is the vertical filter vertical filter basically means if I apply this particular filter on to this image I'll be able to get all the vertical edges in the output image right and after that I've also concerned right well I have also told you if I take an image of 4 cross 4 and 2 cross 2 filter is applied I will be getting 3 cross 3 if I am NOT adding any padding or stripes stripes basically means first of all I place this filter on top of it over here okay so this is my 3 cross 3 so this in the first operation I'll do this I'll get the value what kind of operation is happening convolution will try to multiply each and every value of this suppose 0 into 1 is 0 okay then in the second 1 into 2 is 2 right in the 3rd field 0 into 3 is 0 and like this all the addition will happen and finally we'll be getting the value for this particular field and similarly this operation will happen and that that usually happens in convolution and I have discussed this in my previous videos also now the most important thing to understand over here is that when we are getting the output image right what happens after this particular stage that is pretty much important to us understand and always remember guys like this kind of filters you know suppose this is my filter 1 we may also have another filter like f2 we may have another filter like f3 this filter may be an horizontal edge detection this may be some like shape detection something like this kind of filters will be present now you know that in air and we will try to update this weight will try to learn this weight with respect to the output in the backpropagation stage you know that after we get the output we calculate the loss and after we get the loss what we do we again back propagate we find out all the derivative is subtract all these weights and we update those weights right so similarly in this particular case we will try to learn these sweeteners we will try to update the values inside this particular filter with the help of back propagation and that is the trick over here all the concepts are same everything is same right there is one more like suppose I am getting the output after one convolution operation there are still some more operation called as Matt's pulling I discuss about max pooling in my next video but you need to understand that this filters will have to be learned by my convolution neural network this values needs to be updated inside my filter now once I am getting this particular output after this I will again go and apply relu activation function on each and every field on each and every field I have to apply the rail u activation function like how I did it over here you know so this was my operation right I I multiplied rates with my inputs in the NL after I got this value I applied it I applied an activation function so similarly over here first of all I applied filter I did my convolution operation I got the output after getting the output for each and every field I applied my real activation function right now after when I do the Ray Lu activation function you need to understand in the backpropagation and again I've still not discussed about Matt's pulling but Matt's pulling will also be play a very important role over here so in short when when I just go ahead and finally when my back propagation is done this all values are getting updated a similar case like how we did the updation over here considering the last function will be considering some optimizer same thing will get applied to this convolution neural network also so this is pretty much important to understand guys the basic difference why I am showing you a n and because the operation is almost same you know will multiply the weights with the inputs and then we'll try to apply an activation function so similarly over here in the convolution operation what will happen we will take this filter will apply convolution operation whatever output will go we will will apply an activation function each and every band is over here and then we'll finally get the output so this is the basic difference in the operation of CN N and an en n right after this you know you may also vertically stack any number of convolution so this is one convolution operation right considering the rail you activation function is one formulation operation and I can stack this horizontally you know one after the other now why why do I have to horizontally stack also you should understand that suppose let me take an example we know that in our brain right we have various for various regions to detect some images right suppose I am seeing a image of a cat support and I have explained in my previous video I may have layers like v1 v2 v3 v4 v5 v6 and this is also horizontally stacked you know after this particular output will be given to v2 then it will be given to v3 v4 v5 and finally we will be able to see the image right so similarly in this case we can also stack this or conditioner Electric horizontally one after the other suppose in the v1 suppose I am seeing a cat over here okay so this is my cat sorry for my bad diagram but consider that this is the cat okay suppose v1 is able to detect the face okay then since I have horizontally stacked it with another convolution over here now this this this face will go to the next stage here the more clear phase can be visible then inside this particular phase what are the features that can also be visible right and they may be different different filters for detecting eyes they may be different different filters so we are we can use maximum number of filters over here and you know that initially all this values inside this filter will be randomly selected you know some values will get selected and later on with the help of backpropagation all these values will get updated right so similarly we can stack this and this one whole operation is called as one cognition layer and we can stack this horizontally after this I'll also be discussing about what is max pooling then you learn more understand about why max pooling is also used there is a very very good term in one of the research paper called as location invariant location invariant right so very important term in one of the research paper what it says is that usually when our human brain see some faces right some of the neurons get automatically triggered right and similarly we should try to make our convolutional neural network also do like that so what I'm saying is that suppose in one of the image I have multiple cats multiple cats faces then automatically this convolutional neural network or the kernels that I'm basically using should get automatically triggered you know where it will be able to detect those multiple faces so that step will be done by the max pooling layer okay I will be trying to explain you that in my next video but just understand this was the basic difference between artificial neural network and the CNN I hope that diagram has got completely mixed up but I hope I was going in the right part I have explained in the right part just keep this in mind in my next video I'll be discussing about the max pooling layer now I'll go what I said in the starting life if you are looking for transition towards data science if you're looking for such barrier advice if you are looking for some help respect to some real-world data scientists talk you can basically go and visit this channel called a springboard India the link is basically given in the description it has wonderful discussion with respect to different different data scientists you can have and have a look onto that the link will basic will be given in their description guys so I hope you like this particular video please do subscribe on channel if you are not already subscribed I'll see you in the next video have a great day and given it up