Key steps for Designing Convolutional Neural Network(CNN) for Image Classification

so good evening everyone I am Nishant tiwari part of data science team in United States and welcome I welcome you all to our data session for those who have who are attending this session for the first time let me give you a brief introduction what data is data is a one our dedicated session where we learn about the emerging Technologies on topics related to the data science field for today's session we are going to talk about the key steps for Designing convolutional neural network for image classification what CNN convolutional neural network is like it's a specifically used for processing the image pixels okay so before we move on to this session just let me give you a key a quick recap for the instructions uh we are recording this session and this recording will be available on our YouTube channel you can find the link for the channel in the chat section and uh please ask your questions in Q a section and we'll be taking on those questions like in mid session or at the end of the session lastly we are sharing some polls so please do fill those pool because that will be really helpful for us now for this today's session we have arpit with us he is an international speaker in the field of DS mlbi and AI uh and is currently working with infosy as a senior data scientist he has an experience of 11 plus years in vlsi research and data science machine learning and artificial intelligence so I'll just pass on the stage to the Earth and arpit you can share your screen yeah yeah thank you very much for your kind introduction also allow to share my video as well if possible okay I'll just see to it also please make me purposely possible yeah yeah okay okay thank you very much tell me Maya is visible hello hello yeah am I Audible or not simplest I think it's a network issue but your screen is visible and you are audible please can you check your internet connection one now your screen is visible participants please wait for a few minutes uh just give me one or two minutes I'll check what is happening I think there is some technological issue [Music] we'll start in few few minutes just be there with us foreign hello Sir with your audible you can share your skills yeah yeah just give me a second I'm sharing my screen thank you everyone for having patience we'll just start in a minute just just give me a time because I do not automatically getting disconnected from the session foreign neural network to the participant actually this is my third talk series before that I have already delivered to talk first talk I have delivered that key step for Designing the artificial neural network then after that again I have delivered the second talk on how we are using the artificial neural network for image classification and now this is a third talk which I am giving on key steps for Designing the convolutional neural network I request all the participant for the clear understanding if you will go through my previous few talks that is two talks which I have delivered then you will understand a very good way this session so in my today session I am going to discuss about the main important concepts of convolutional neural network and how it is useful in our day-to-day life for moving I would like to say [Music] something [Music] architectures are available for the control neural network important question is that going to what can you check your internet connection once because your voice is breaking at I think there is some disturbance from Earth's side let's let's wait for him and we'll continue this session sorry guys for this this uh disturbance will continue this session and let's wait for he's having some internet issues uh Saga hello I'm getting lots of disturbance actually automatically I am getting disconnected I really don't know why I'm trying my label ways to solve but again I got disconnected sorry I got connected now so that's third decision I don't know why I am disconnecting okay okay you can continue yeah yeah just give me a moment I'm just going to share my screen up s foreign now can you please confirm Sagar my screen is visible yes yes indivisible okay thank you very much okay so I'm starting sorry for uh disturbance guys sorry uh sorry for all the inconvenience so I'm starting my session again uh I welcome all of you for our today's session called keys test for Designing the convolutional neural network and I'm going to guide you all the key steps for that so before begin this session I would like to say something about me my name is arpit yadav and I'm working as a senior data scientist at in Sophie Bangalore other than that I'm Associated as a mentor with multiple companies and universities across the India where I am working as a C2 at aibi Bangalore I am also working as a guest faculty in artificial intelligence and machine learning at bits pilani I am working as a mentor in AIML program at IIT guwahati other than that I am Associated as a mentor multi with multiple University for Designing the AIML process now about my education qualification I did my bachelors in electronics and telecommunication engineering and Masters in vlsr design I did my pgp in artificial intelligence and machine learning from Texas University USA and I'm currently pursuing my PhD in machine learning in the field of deep neural network only I am having the total 12 years of experience parallelly along with my main job I'm having the eight years of experience into the skill development training I am an international speaker in the field of data science machine learning deep learning and AI I have filed for patent tildent in the field of artificial intelligence and machine learning and I have published so many International Journal Publications as well I did 70 plus certification in the field of AI whatever the small contribution I have given to my field I have been rewarded with these four Awards which motivates me a lot to do something better for the society in that I received the award as an inspirational data scientist award for AIML work only and then again I have received the Excellence research award why the progress Global foundation for some paper Publications then I have been awarded as a base corporate trainer award and the best in Innovation training and design award so these are all Awards which motivates me a lot to do something better for the society so let's go ahead for Designing the main concept today that is called as a convolutional neural network but before moving ahead whenever we are doing any projects in the field of deep neural network this is a motivation slide for all of us in this slide with the help of this equation we are solving the N number of projects in the industry so what this slide tells about this slide tells about that the heading of this slide it new normal in the technology guys new normal in the technology what is the exact meaning of New Normal in the technology first of all let's understand the meaning of New Normal the meaning of New Normal is very easy New Normal means what anything which is coming new to the world and becoming normal very easily is a new normal technology or a new normal Concept in the technological World New Normal means what for example artificial intelligence technology is a new normal technology why it is called as a new normal technology because in this new normal artificial intelligence is very new to the world and become very normal easily in the field of many applications because of this reason many Industries are using the artificial intelligence and it is a new normal in the technology so to understand that in this life there is one equation is written let's understand the meaning of this equation very simple whenever we are adding data science machine learning deep learning and artificial intelligence to Any X we are always going to get a profit and the growth guys okay so again I need to ask the audience from your end what do you think about what do you mean by X about what do you think guys what do you mean by X in this slide any answer for that industry okay very good Bala has given the correct answer industry X means any industry for example you put you put healthcare industry in place of X so you are always going to get the profit and growth there how in the healthcare industry we are making n number of application with the help of artificial intelligence technology for example you people are going to make a robots which are going to do surgery that is called as a robotic surgery you can make n number of apps with the help of AI technology to detect the deadly diseases in advance so that you can save the life of people you can put banking industry in place of X you can make so many applications with the help of AI technology whether to give the loan or not to the person okay we can identify the defaulters in the bank industry with the help of AI technology so that industry in the world of data science and machine learning is called as a domain we are using the word as a domain to them hence that equation will become adding data science machine learning deep learning and AI to any domain you are always going to get business and profit hence to make any successful project in the world of data science machine learning deep learning and AI there are two important knowledge required one knowledge is called as a technical knowledge related to the field of data science machine learning deep learning and Ai and the second important knowledge required is a domain knowledge in which industry and in which process you are going to apply that knowledge so domain means industry knowledge for example suppose I am working on the project called as cancer detection using machine learning algorithm so cancer is a healthcare domain and I am a non-healthcare person I am an engineer I didn't know a bit about cancer so I cannot able to solve this project without understanding the cancer knowledge or we can say a domain knowledge hence we are taking the help of domain expert in the industry and then what your knowledge I am having in the field of AI and with the help of domain expert we both are solving that projects so remember guys very crucial domain expertise is extremely important in the field of AIML technology to solve the project if you are not having the domain knowledge of that part so once it is there then we are going ahead for the further part so please concentrate this is called as a road map to learn the Deep learning technology guys and as I said please attend my two previous session uh I think that you can contact analytics with the only for the link of my previous position in that this roadmap I have discussed about and if you are very newcomer in the field of deep learning please follow this roadmap blindly because this roadmap is the best direction to learn the field of deep learning in that it is said that first of all you need to understand the biological neuron working biological that means I am talking about biology subject we need to understand the biological neuron because our entire deep learning world yet inspired or motivated from biological neuron only scientists has taken the inspiration from that and they have made the first world artificial neuron that is called as the perceptron of the world after understanding the working of perceptron you people will came to know the three important things or terminology inside the perceptron that is called as a weight bias and activation function uh due to the time limitation I cannot go in detail about that I have already discussed this in my previous talk Series so please go to understand where bias and activation function but there are certain limitations of perceptron for example logically I am asking to you question please tell me everyone in our human body there are billions of biological neuron available billions of biological neurons from where the scientists has taken the inspiration Omega artificial neuron so my body right now in my body all the biological neurons are communicating in such a way that I am giving the talk to you people similarly all the biological neuron in your body is communicating in such a way that you are listening to me so one simple question I am asking does if in my body only one biological neuron is present will my body will function or not tell me the answer or your body will function or not only one biological no one biological neuron not able to function our body hence we required the billions of biological neuron out of which a good biological neurons are getting connected with each other to perform some tasks same in the world of artificial neural network single perceptron is not sufficient to solve the complex problem that is called as a non-linear problem perceptron is a linear classifier hence in order to solve the non-linear problem or complex problem in the field of deep learning we are using the multi-layer perception that is also called as artificial neuron Network so artificial neural network I have already discussed in my previous talk so please go ahead and watch that so artificial neural network is working in the two methodology guys that is called as forward propagation and backward propagation forward propagation is used for prediction that predicted value we are comparing with actual value we are getting the error so to reduce the error we people are doing the backward propagation and during the backward propagation optimizers are used in the field of deep learning so that understand all Optimizer one of the optimizer is a gradient descent then socastic gradient is an atom undergrad RM is profile so on understand that after that we will come to know how to design the ANL with the help of sequential and functional API there are two kind of Designing in that I have already discussed in detail that now today as we are moving ahead step by step process for the further designing of deep learning architecture so again guys a n is having certain drawbacks artificial neural network is having certain drawbacks which is overcome by the other part so the thing is that in this road map after a n artificial neural network having certain drawbacks which is overcome by other architectures so what are those other architectures so other architecture is called as for example CNN architectures which is specifically designed for handling images and video kind of data and a field which is related to images and video kind of data is called as a computer vision so CNN is the basic architecture for that CN for the computer vision field and other architectures are available I will guide that don't worry about that other architecture is called as the RN and recurrent neural network which is specifically designed for natural language processing problems sequential data analysis so there are also other architectures available called RNA and lstm bi-directional lstms Transformer bird gpt3 and so on and again after that there are more architecture design for specific application that is called as a gan and so on so what I mean to say that please follow this guideline please this step by step procedure in order to understand the real world of deep learning you will really enjoy that and then do the small project do the small case study by understanding all the concept mathematically so let's go ahead as I said you when we are going for CNN so a n is having certain drawbacks so what are those drawback guys artificial neural network designing is based on fully connected layer Fashions if you go through learn the a n and designing it is having the fully connected layer fashion due to that reason we require the more computational systems to perform the computational mathematics inside that so for that process only we required more Hardware dependency on the a n there is no parameter sharing inside the an architecture in an architecture is not work good on image data in the sequential data why because I will let you know what is the major problem of that so artificial neural network is not working good on unstructured data that is images and video okay although if you try to work on rtps and neural network of images and video it will give the output but it will be not Optimum output as compared to other architecture so that is a meaning that we are having the certain limitation for a n and that limitation is then overcome by other architecture and in the Deep learning world that application for the other architecture is called as this one application is called as the computer vision application one application is called as a natural language processing application one is called as the gan so in the computer vision application what we are doing we are giving the ability to computer or the systems to visualize our real world in terms of images and video and from that they are understanding something from that images and video to do certain outcomes similarly in the natural language processing what is going on we are giving the ability to computer assign the systems to understand read human languages interpret something from that and give the outcome accordingly so that is the main process so in this part actually these are the use cases of the deep learning major use cases of the deep learning computer vision and natural language processing and so on as I said for the computer vision application researcher has made intentionally one architecture that architecture basic architecture is called as convolutional neural network which is specifically as I said designed for images and video and there are other application in the field of computer vision that I am going to guide you so which architectures are used for that but also you should know so in the field of computer vision image based application are majorly used video is nothing but the sequence of images only and so on so concentrate carefully what I am guiding in the field of computer vision we are having so many applications such as image classification image segmentation object detection Gan and so on so whatever the application we people are having in the field of computer vision we are having the special architecture designed for that you no need to design the architecture if you want to design the architecture that is a research button you design your own architecture but the technology advancement has been taken place in such a fast speed that whatever the application you people are going to work for computer vision already readymade architecture available in the market for example as I said image classification in one of the application in the field of computer vision so we are having the basic architecture called as convolutional neural network for image processing other than that also there is concept called as transfer learning transfer learning means what there is one competition in the world that is called as the imagenet competition and the winners of that imagenet competition is declared as a state of art algorithms or a state of art architecture and that architecture is again useful for the image classification based problem again other application in the field of computer vision is image segmentation again there are predefined architectures available for example RCN and fast rcnn faster rcnn unit this architecture is specifically designed to solve the problem of image segmentation other than that third major application is called as the object detection in the field of computer vision for object detection also there are many architectures available in the market but the famous one is SSD for example single shot detector YOLO and its version and the last application in the field of computer vision as well as the Gan generally tell you adversial network unsupervised learning so the what I mean to say that there are many applications as I am highlighting the major one so in the field of computer vision for this four major application image classification image segmentation object detection Gan measure architecture has already given by researcher to us so all these architecture fundamental is that this all architecture are born from CNN this is the major part that's what today's reading is key steps for Designing convolutional neural network all this architecture if you will go through after my talk you will find us this architecture is nothing but CNN only convolutional neural network and so on so the thing is that today major motivation is to learn CNN architecture that's what we people are learning CNN because entire computer vision applications depend on CNN so in the CNL today we are going to learn about what are the key steps to understand the CNN and so on and I am going to guide you in a very easy way so please concentrate carefully convolutional neural network is nothing but one a n and only artificial neural network only the key difference is that in the artificial neural network we are having the structure input layer hidden layer output layer as in the convolutional lateral neural network we are changing the entire hidden layer architecture and this is our hidden layers in the field of convolutional neural networks convolutional neural network having the four major layers we can say majorly three that is convolutional followed by non-linearity activation function called reloop so the first layer in the field of convolutional neural network is convolutional layer itself second layer is called as the pulling layer or sub sampling layer and the third layer is called as a fundi connected layer what is the basic need of all this layer that I am going to guide you in the high end don't worry so just remember that Foundation of convolutional neural network is this three major layer convolutional followed by relu this is one layer second is called as a pulling and sub sampling and the third is a fully connected layer so for what purpose all these three layer use and what is going on inside that let's understand that and why the name convolutional neural network has been given the name convolutional that depends on convolutional operation only so let's take it so please concentrate guys in this slide I have given one image for some certain task that is image classification for example so how this image will preprocess from input layer to Output layer and at the output layer image get classified so how the things going inside the convolutional neural network let's understand that so actually in the convolutional neural network there are the two part okay first part is polarizer feature extraction always remember note it down guys if you are having something to note down convolutional neural network working is divided into two major part first part is called as a feature extraction part and the second part is called as a classification part noted that first part is future extraction second part is the classification so who is doing the feature extraction part very simple as I told you these are the three main layer of the convolutional layer so the convolutional layer is responsible for extracting the important features from the images that is a convolutional operation second pooling layer is responsible for reducing the dimension of image and for third fully connected layer for classification so let's understand that all the part step by step what is the meaning of convolutional actually so I want to ask instead one question to all of you how do you people learn convolutional mathematics in your study or not in your graduation or anywhere just one simple question no you people have a convolutional operation okay no worry about that I will guide that okay so the thing is that an evolutional is nothing but one mathematical function or mathematical operation which is used for doing certain kind of mathematics just I am going to guide you just wait this is called as a convolutional operator please concentrate on this actually image is nothing by guys two dimensional function okay image is nothing but two dimensional light intensity function when numbers are there in the images pixels value so on this image please concentrate on the left hand side there is one image guys there is one image blue color Okay in this image there are some numbers this is called as a pixels number image is nothing but pixel representation guys pixels is having number and the pixel full form is picture element so what we are doing that to this original image which is highlighted in this Slide by the blue color we are applying one mask so please understand carefully the meaning of mask is what and different terminology used for this mask so please note down mask is also called as kernel filter feature detector or feature extractor four to five different names are there for this green color three by three mask in this image again I am repeating image is there in the convolutional neural network which is highlighted in this diagram by blue color images having pixels value and we are applying one mask in that so mask is also called as kernel feature detector filter feature extractor so in this mask there are again some numbers are there what is those number actually and what those number represent so please concentrate again very carefully these numbers are nothing but researcher has analyzed something and this number is going to extract some meaningful information from the images okay this meaningful information from the images so always remember as when we are applying this mask to this image so mathematical convolutional operation takes place convolutional operation nothing but dot product guys dot product concentrate carefully so when I am going to apply this Mass point to this original image three by three so whatever number of the mask and the number of image that is called as the pixel value is get multiplied that is a convolutional DOT product and finally you will get one number and that number is get replaced to the original equation so that is the meaning of convolutional operator and just I want to show you with the help of some diagram again so that you will easily understand that please concentrate carefully again in the left hand side original image of the size five by five by five images is having some pixels value to this original image of the size five by five I am applying one mask here see in the middle there is one mask kernel filter feature detector feature extractor any name you can give there is some number written inside the mask one one one zero zero zero minus one minus 1 minus 1 what is this number and who is deciding that so let me give the answer of that this number we cannot decide randomly this number is decided by so much research because this filter number is nothing but extracting some meaningful information out of that so guys when I am applying this Mass to the original image so check it my mask is of the size three by three and my original image of the size five by five so this three by three I am applying to the original image so in the left hand side see one highlighted three by three window you are observing so the number inside this mask and the number inside the image pixel get multiplied so it says check it how it is get multiplied this 7 get multiplied with the 1. plus 2 multiplied with the 0 plus 3 multiplied with the minus 1. plus 4 multiplied with 1 and so so last 2 multiplied with minus one so we are going to do the sum of product answer is coming 6 so we are going to give the answer 6 here on the right hand side see here in this case so the thing is that this 6 is called as feature map whatever the pixel number are there it is called as the feature map so now please note down in your notebook in the convolutional neural network first layer is called as convolutional layer followed by activation function called as reloop so what this layer is doing concentrate we are applying some filter to the original image filter is having some size original is having some size so the formula is telling that image multiply by the filter is equal to feature map note it down guys very important image multiply by the filter is equal to feature map and again I am asking out of curiosity what is the different name of mask can you tell me what are the different name of mask kernel filter feature extractor feature detector okay and when we are applying to this original filter what is the output output is called as a feature map and what is the feature map let me explain you the meaning of feature map and let me explain you what is the meaning of all this filter and the number so as I told you scientist has designed this filters guys this number is not randomly decided let me clear you this number came by doing so much research and when I am applying this number convolutional operation to original image so this number is extracting some information from the image that is called as feature extraction that is called as a feature extraction and in this case guys again please note down one important thing in the convolutional neural network that is first layer called as convolutional layer here here are two hyper parameters one hyper parameter is called as what is the filter size filter size we can decide that's what I am saying filter size is nothing but hyper parameter and how many filters are available for this operation that also we can decide so please concentrate in this image although I am showing the operation with the visualization for one filter on one image equal to feature map but it doesn't mean that only one filter we are going to apply we can apply 30 filters also so we will get 30 feature map for one filter one feature map so the thing is that guys hyper parameter in the convolutional is size of filter and number of filter note it down size of filter and number of filter although in this image I am showing the example of one filter applied on one image equal to one feature map but in real world we are applying multiple filters for example if in the same diagram image is there on the left hand side one image but I am going to apply 30 filters so how many feature map we will get how many feature map we will get 30 very good now you got it so one filters are used for extracting one information from the image analyst understand that what is that very simple now so please concentrate this is that for example in this image concentrate carefully left hand side is one image original image to this left hand side original image of whatever the size of the image is I will apply one vertical filter to this image vertical filter again suppose three by three having some numbers in that so that three by three vertical filter number is going to extract only vertical line from the original image so take it on the right hand side vertical edges so what is happening guys behind the scene in the convolutional operation if you are extracting some meaningful information from the original image and with the help of what with the help of filters with the info filters now are you understanding or not tell me first see again I am repeating original image is numbers filter is a mask that is having some number that number you are multiplying to the original image that after that one multiplication it will expect some features from that so the entire operation as well as convolutional layer operation so the formula of the convolutional layer operation understanding the formula means it is some equation image multiplied by the filter is equal to feature map image multiply by filter is equal to feature map and one more thing please note down one more formula which I forgot to tell you which is very important in this slide left hand side is one five by five image and some random numbers are there we are multiplying to this five by five image three by three filter so you are getting feature map is also three by three so how it is taking place so note down the formula please note down the formula suppose the size of image is n by n small n by n the size of filter is f by f so the output image you will get is please note down n minus f plus 1 multiply by n minus f plus 1 I am writing in the chat window okay I am writing in the chat window to everyone the formula will be like this n minus f plus 1. this will be the formula for feature map what is the n what is n N means email price what is f what is f f is filter size and original out of this feature Map size is what n minus f plus 1. so please check according to this formula 5 by 5 is the input image size multiply the three by three filter size so Phi U is small n 3 is small F so multiply sorry do put in the formula 5 you multiply by 3 plus 1 what is the output 3 so you are getting three by three feature map you are getting three by three feature map so I am writing in the chat window final formula for extracting the feature okay yes Akshay you are writing one more uh means of formula for having the spread and the padding also I will come on that don't worry slowly I am teaching so the final formula for the feature map is n minus f plus 1 according to this example right now I will come for the further also okay n minus f plus 1 note it down multiply n minus f plus 1 this is the part this is the feature map formula as per this example okay note down very important and very easy also so let's go ahead so this convolutional neural network first layer is convolutional and we are working on how the convolutional is extracting the feature now please concentrate again on this example six by six is the image size 3 by 3 is the filter size so what will be the feature Map size tell me the answer four by four very good now you got it again please concentrate carefully the number inside the filters who is deciding that we are deciding or it is a research part it is a research research researchers has already given some filters to you people and that filter number is already decided for example search in the Google Sobel filter so Sobel filter is having some number already okay super filter is having some number already so check it in the Google you will get that so please concentrate carefully for example in this original image multiply this filter is equal to feature map so the number inside this image is decided by researcher you can also design your own filter what that filter number should extract some meaningful information otherwise randomly giving any number to filter and that filter is nothing doing only mathematical operation and not extracting anything so is there is use of any filter tell me logical if I am giving any random number to filter number and that filter number is multiplying with the original image and not giving any meaningful information so there is no use of filter so that's what I am saying researcher and designing n number of filters doing n number of experiments and finally they will come with such a number of filter which is extracting some meaningful information from that so this is the convolutional layer operation guys and I hope you understood this yes or no so check this in this slide number of vertical filter and the number of horizontal filter check it just for the knowledge purpose in this slide other example are diagonal filter color filter edges filter counter filter and so on so in this slide there is a number that number is detecting vertical edges horizontal mask is here on the right hand side that number is detecting horizontal and so on this is called as guys filter operation yes understood or not tell me that then I am going for the other part so now concentrate carefully so once you will get this output as a feature map from the convolutional layer okay so again we can apply pooling layer to the output of convolutional layer that is to the feature map Now understand this diagram as I told you entire convolutional neural network operation is divided into two major part first part is feature extraction other part is called as classification who is expecting the feature actually convolutional layer is extracting the feature with the help of that filter output is a feature map that is called as a feature extractor that sorry features get extracted now to the output of convolutional layer we are applying pulling layer we are applying what pulling layer pulling layer is nothing but it is reducing the dimension of image that's it hence in this diagram it is said that combination of convolutional and pulling is for use for feature extraction and after that pulling layer we are giving the 2D image converted to 1D then it will become fully connected layer at the moment it will become fully connected layer from that moment it will go for classification part so convolutional is this price in the front of you classification plus feature extraction if anyone asks you the question who is doing the feature extraction originally convolutional layer is doing the feature extraction and reducing the dimension of this feature extracts and pooling is doing what feature extraction is only convolutional is doing okay hence this is the meaning of that guys so the thing is that in the field of convolutional neural network these are the important terminology so why we are using the pooling layer to reduce the dimension of feature map again I am repeating feature map okay and what is the feature map output of convolutional operation understood now are you getting very easy okay so now concentrate carefully very easy that in the pooling layer how many pooling operation is possible okay how many pooling operation is possible so let's take an example left hand side is the feature map in this slide there is the left side is the feature map of the size four by four just example and I am applying two by two pulling layer I am applying two by two pulling layer in this example just consider the left hand side image is a feature map output of convolutional layer again I am repeating output of convolutional layer is the feature map to this feature map suppose I am applying pulling layer of the size 2x2 so you tell me in this image I have applied two by two pulling layer to the original image that is called as feature map so there are four section green color red color and blue color and some wallet color in the two by two pulling size into original image green color which is the highest pixel tell me that which is the highest pixel line so see in the output of pulling layer 9 is there ninth why because I applied the max pooling layer Max pulling similarly in the red color two by two window which is the highest pixel 2 so 2 is get forwarded again in the Violet section 6 is the highest pixel 6 is get forwarded again in the bottom two by two threes get forwarded so this maximum number pixel value is forwarded to the resultant image this kind of process is called as Max pulling because it is taking maximum pixel to the output this is called as a Max Cooling and what the max building is doing just check simple answer what is the size of feature map 4x4 and what is the output of pulling two by two so pooling layer is reducing the dimension of image for that purpose pulling layer is used just and why we are reducing Dimension any logical answer any logical answer please tell me suppose in the 4x4 image how many in the four by four image how many total pixels are there very good actually has given the answer to reduce the computationals very good answer that is the meaning of that so not only Max pulley we are having so many options average pulling minimum pulling and Max pulling and other customized pulling also according to our need so max pulling is taking the maximum pixel value from the image minimum pulling is taking minimum value average pulling is taking average value so this is the part of that so the thing is that these are the entire part of feature map apply the pooling operation to the feature map you will get the max pooling as output average pulling as output or some cooling as the output this is called as a pulling operation price okay now after that in look field of convolutional neural network also there is two major operation of inside that we can say stride operational say stride what is the meaning of stride let's take an example and what is the meaning of padding that also take an example in my previous all example okay for example let me come here to the original convolutional operation so that you will understand the meaning of that in this example please concentrate carefully original images of the size five by five filter is the size of three by three when I put this three by three filter into my original image so this blue color images get sorry we can say blue color is this highlighted so that pixel is get selected now sixth number is the output of that after doing the multiplication of all filters value with the original image if I will move the filter one step right side so this this will be the three by three next three by three it is concentrated on my cursor 2 3 3 5 3 8 3 2 2 just I have shifted one moment right that is called as I stride guys what it is called as stride shifting your pixel value one moment right so let's check it let's check it how it is shifting see again I am repeating your three by three window is just shifting one one step right side and you are getting feature map as the output so this filter as it is moving the movement one step right side and then one step downside movement of one is called as a stride of one is this clear this is called as I stride of one so if stride is coming into picture so the size of output that is called as a feature map formula will become like this so I am writing in the chat window actually the total main formula for feature map is this please concentrate n minus f plus 2 p divided by S and then Plus 1. this is the major formula final formula for feature Map size n minus f plus 2p I have written is equal to please correct the formula in the chat window n minus f plus 2p divided by S Plus 1 this is the formula I will written in the slider so don't worry so this is called as the stride operation guys the moment stride is done then again I am going for one more operation called as padding so for what purpose padding is use concentrate careful again I will go to the same side to explain the operation of padding so that you can easily understand please concentrate again this slide tell me guys in this slide original image size is what what is the original image size Pi by five what is the resultant feature Map size three by three feature map so actually original images gets shrinked to three by three so convolutional layer is also reducing the image size yes or no I am not going for pooling right now so convolutional layer is also reducing the image size so 5 by 5 is reduced to three by three image gets shrink if I want that my convolutional operation should not reduce the image size 5 by 5 apply the filter output should be also five by five if I want this then I will use padding I will use padding guys again again I am repeating to keep the size of image same in the convolutional operation because convolutional operation is also doing the size reduction of the image so for that purpose we can use the padding or socialist concentrate on that how it will work so padding is very easy we are just adding the zero layer at the corner pixels of original image like this check it original images of the size six by six okay 6 by 6 and we are adding the 0 to the boundary of image this is called padding of jio what it is called as padding of zero so if we are going with the padding of 0 then your filter size sorry your feature map will not reduce feature map will be the same size of input guys feature map will be the same size of input so just for the time limitation I cannot go ahead for the detailed explanation of that I am requesting that just take one five by five image apply the zero zero zero zero this padding layer to the five by five image and apply three by three filter to that and you check the output by your own you will find that output is also five by five hence size of the filters size of the image is not reducing so there are two kind of padding guys two kind of padding okay padding is used to for a two purpose as I said to retain the size of image someone has the question to answer is to retain the original size of image plus to retain the information of boundary pixel to retain the information of boundary pixel padding is used okay so don't worry so padding is having two major applications that is to retain the size of image and to retain the boundary of pixel so the thing is that guys padding are of two type valid padding and same padding how many type paddings are available in the CNN valid padding and the same padding valid padding means no padding actually we are not using padding there and same padding means we are using the padding like this this operation so this is the example of same padding in this slide it is called a same padding same padding is at the layer of zero at the boundary of the pixels same padding and valid padding is no padding so at the end of the day this is the final formula of the image by considering padding and so on so this is the example of padding guys check it in this example I have added the valid padding to the five by five image and then I have multiply three by three filter and when you will do entire operation you will get output also five by five you will get output also five by five is this clear so this example which is available in this slide is called as same padding what it is called as same padding are you getting or not please tell me although I know that I am doing some little bit fast but I want to know are you dating or not so in this slide this is the example of same padding for what purpose padding is used to retain the image size again I am repeating to retain the original image size and the boundary pixel information that is for padding is used and at the end guys by having all consideration of padding and so on okay I am having the final formula available this is that final formula okay this one in this slide please concentrate final formula of the feature map when I am considering padding when I am considering stride and so on this is the final formula okay this is the feature map guys this is the convolutional layer operations so convolutional layer in order to understand convolutional layer is having the two major thing one is called as the filters and the size of filters and total number of filter that I call as a filters so if anyone asks you in the convolutional operation what are the hyper parameters what will be your answers tell me what will be a size of filter number of filter stride and padding also yes or no now this is final convolutional I I haven't went to pulling it again I am repeating only in the convolutional operation how many things possible again I am repeating number of filter size of filter tried and The Paddy yes or no all are hyper parameters and very easy so I can decide how many filter I can use I can decide the size of the filter I can decide shall I use the padding or not I can decide what should be the stride one two or what and these four major thing is the key step for Designing convolutional layer now I hope you understood the meaning of convolutional layer then output of convolutional we are applying only so now second layer will come into picture pulling are you getting now yes respond so write down in the sequence first is convolutional in the convolutional four major part size of the filter total number of filter stride and padding very good then output of this we are going for we can say uh pooling layer pooling layer is reducing the size of image that is size of feature map and output of the pulling we are feeding to the fully connected layer so at the end of the day you are having the final structure available in front of you that is called as the world of convolutional neural network check it now in this diagram check it in this diagram what we are doing original image I am applying convolutional layer inside convolutional there four things are coming size of filter total number of filter is tried pulling output of convolutional layer is feature map apply pulling layer pulling layer is reducing the size of feature map to half again output I I can apply one more convolutional layer because this is the hidden layered architecture now I can put n number of convolutional layer it is up to me and a number of pooling layer it is up to me that's what you are a designer of your own convolutional layer okay you are the designer of your own conversational layer okay so the thing is that in this architecture please concentrate carefully I am asking one question logical question to you how many convolutional pooling layer used in this architecture tell me that very good two stack two stack of convolutional pulling is used in this architecture now the same architecture I can design in such a way please concentrate convolutional layer convolutional layer again after that pulling layer then convolutional layer pulling layer I can do like this also it doesn't mean that after the convolutional layer compulsory always pulling layer you need to apply you can do one thing also convolutional layer again convolutional layer then after that pulling layer then again convolutional layer pulling layer convolutional layer pulling layer you are a designer of your own is this clear but blindly we are not doing all these guys we are at this doing all this designing related to problem statement under domain knowledge so then you are using how many convolutional layers should I use shall I apply pulling or not shall I apply any other items process in the architecture so you cannot doing that blindly you are doing something with the help of some previous knowledge is this clear or not so now one again logical question the combination of convolutional and pulling layer is giving what information to you people is given what information in this slide it is the features very good I appreciate you features that is called as an important feature now again one logical question inside that who is actually responsible for expecting the features who is responsible for expecting the future from the original image filters I really appreciate tiger your answer is correct filter actually convolutional layer but inside convolutional layer filter at the end of the day yes or no in the convolutional layer only filters are there so main thing is filter filters are used to extract the important features from the data and then you are going ahead for that at the end of convolutional pulling operation we are getting the important features so that important features now we are converting to one dimensional that is called as fully connected and then it is going for the classification so fully connected is responsible for classification and no no I have not frozen the screen actually I am stuck in this spring screen only don't take tension so the thing is that that convolutional pooling layer used for feature extraction that feature extraction whatever we are doing is converted to one dimension sorry yeah yeah one dimension and then it is connected to fully connected part which is doing the classification part this is the world of convolutional neural network and I would like to show uh something to you I have already I have already shared uh one the Jupiter notebook with you I hope you are having that Jupiter notebook with you okay open that Jupiter notebook I will explain you something okay so the thing is that again I'm requesting the organizer please give me one more session to explain convolutional neural operator on color image I have explained today convolutional neural operation on one one channel that is one of the grayscale image okay I am requesting give me one more session for color image also so guys whatever I have explained today is by considering for what purpose which part gray skin image okay and so on color image I will explain you after this session actually okay because in this session due to time limitation is not possible so guys please go and open the Jupiter notebook which I have shared with you people and the jupyter notebook is this one a n n versus CNN for image classification please go ahead okay you can search my name in LinkedIn with the name called as arpit yadav senior data scientists upgrade in Sophie yeah yeah you can plan masterclass also for three hours if if someone's analytics Visa is ready to give me three hours I will do entire postmortem of this as analytics Vidya as they I don't know that organizer are listening or not please open this you you only people request to organizers okay then they will give you three hours then three hours Miss end time postmortem research work for CNA don't worry so please open this and write down in the chat window I hope you are understanding although I have gone little bit fast but I assume that you are understanding in the clear way so if you open this I am going to explain one project that project I am going to logically prove you that how CNN is better than a n n for the same project for the same project okay so please concentrate carefully what is the session about okay yeah I can able to help anyone don't worry just connect with me LinkedIn Priyanka darbari connect with me Lindy okay don't worry I will help you out so the second the this project is that we are going to do image classification we are going to do the image classification of handwritten digit data set with the help of a n and CNN okay vnn and CNN so in the n and CNN I am going to tell you that CNN image classification because data is a image now please concentrate carefully what is this project about this project tell us that we are having now please consider I am writing here so that you can understand we are having 70 000 images seventy thousand images of handwritten digit 28 by 28 grayscale image and we are having 70 000 label associated with that this is the data set description I am showing we are having 70 000 images images as an input and 70 000 label as output all the images is of the size 7 28 by 28 grayscale image this is the data set given to us so 70 000 images with seventy thousand label which are those image handwritten digit image now we have to design image classification a n and CNN architecture to classify this image so how many digits are there guys how many digits are there this is the MD's data set absolutely correct open source data how many digits are there tell me 0 to 9 total 10 digits so we have to design a n and architecture and CNL architecture to classify 10 digits so this is the problem of multi-class classifications what is this problem about multi-class classification because I have two classic by 10 digits okay now uh yeah yeah I'm just going and wrapping the session no worry so say this is the project so this is the multi-class classification project so what I am going to do guys you are having the jupyter notebook with you just import all the necessary packages which is required to design the architecture for example tensorflow Keras might not leave library for a data visualization and all these layers modules and so on so we are importing the necessary packages for Designing the architecture that is a n and CNN now please run this parallely along with me those who are having this Jupiter notebook my voice is breaking for everyone or only for Sandhya my voice is audible or not uh please let me know why is this breaking for everyone okay Sagar my voice is clean now it's clear you can like in starting it was like everyone okay now it's clear yeah now it's clear you can continue okay okay thank you very much it's breaking again yeah your voice is not already I think it's making a game okay again again I am repeating my voice is clearly Audible yeah now it's now it is okay I'll repeat you there a bit just a minute is here I think he's reconnecting so why is this nearly Audible I bet your voice is breaking much very much [Music] clear the noise breaking uh okay I'll just give me okay can you please again tell me my voice is clearly audible now am I audible guys hello yes sir your voice is audible oh no no please just give me for five more minutes agar okay just five more minutes I will not take more than that so that I can explain this to them okay so please concentrate carefully everyone I due to time limitation I cannot go in detail just request the organizer provide more time in future so what is this project about guys actually in this project I explained we have designed the CNN and alien architecture for multi-class image classification problem and this is all data set we have imported from Keras it is automatically divided into training and testing part 60 000 images going for training 10 000 images going for testing and just we are doing some basic data exploration part and guys what we did in this project we have designed this a n and this is called as a n not CNN this is fully connected layer in this architecture there is one hidden layer of 100 neuron so input layer is input neuron is having 784 neuron connected to 100 hidden layer then it is connected to 10 sorry it is connected to one hidden layer of hundred neuron and it is connected to Output layer of 10 neuron this is a n with this architecture of a n a when we got go for the model training we got for 10 epox last about 99 accuracy for the last paper tenth number okay for this same project for the same project guide this is Now CNN please concentrate guys this is CNN here CNN required how many which data CNN required guys please tell me please tell me today I talked to CNN layer for only CNN layer what we want filters filter size right and padding yes or no these four things we required for convolutional so concentrate carefully here in this line of Jupiter notebook this is the convolutional layer designing this convolutional layer this 30 is nothing but 30 number of filters concentrate I can give here 60 also I can give 100 also 64 also it is up to us these three by three is nothing but kernel size that is called as filter size concentrate very easy guys don't fear about coding very easy so three by three is the kernel size because as I told you today convolutional layer one kernel size number of kernels that I have given in the same line at the end I can write down comma padding equal to same like this padding equal to valid or saved like this I can write down like this also so I I am not writing any padding here for example so by default it will take valid padding valid padding means no padding and activation is radio so this is called as designing a convolutional layer and to this first convolutional layer we are giving the input 28 by 28 because data set is of 28 by 28 are you getting now this one is called as one image is at a time coming to this okay fourth dimension and now this is called as a convolutional layer operation and now take it input to this convolutional is original image of 28 by 28 okay concentrate carefully my question okay input to this convolutional is coming 28 by 28 so in the first convolutional layer how much size we are applying filter size three by three so what will be the output of feature map 28 by 28 3x3 is the feature that is called as feature detector what is the output 26 by 26 so check it here at 26 by 26. in this model summary output of first convolutional layer is 26 by 26. and I told you one filter into original image one feature map if I will apply 30 filter to the original image how many feature map here in the first line concentrate 30 feature map sorry 30 filters three by three to the 28 by 28 so check in model summary I will get feature size of 26 by 26 and 30 feature map now this is called as convolutional layer guys now are you understanding logically also see here mathematical also I am explaining everything and very simple also immediately to the output of convolutional layer that is 26 by 26 image I am applying Max pooling of 2 by 2. so I am asking to pulling layer hey reduce the convolutional output to half because Max pooling is two by two so output of convolutional is 26 by 26 by 30 apply Max pooling output of Max pulling will be 13 by 13 by 30 so check it output of Max pulling here 13 by 13 by 13 are you getting now okay very easy is this hard or easy okay again I am repeating convolutional operation this is the convolutional designing we are doing this is the first line is a convolutional layer and I taught you that in the first line of convolutional we are having four information number of filter size of filter stride and padding stride hand padding 30 means 30 filters I am applying 30 different filters horizontal filter vertical filter uh diagonal filter is detector color filter and so on this three by three you can change to five by five also seven by seven also it is up to you instead of 30 I can write down 90 also because these are hyper parameter okay if I will not apply this one guys okay or any padding information are described by default it will take valid padding valid padding means no padding sorry uh padding is there so in this again I am repeating sorry please concentrate padding if I am not applying what is the padding used for here so by default it will consider valid padding okay valid padding same padding means we are applying the padding actually so please okay I am concluding the presentation as for the organizer uh decision so I hope that you people are understanding in the clear and faster way that 30 number of filters filter size three by three padding and the slide in the convolutional layer and in this output is Automax yes guys there is there is no sigmoid here it should be salt Max now these two these two in this layer now please save me last question I am asking in this convolutional layer designing how many convolutional pulling layer I have used 1 so I can do one thing also in front of you I can copy this see here I am copying this and I can write down here Ctrl V also now second architecture design in front of you within one second I what I did convolutional pulling now again I have added convolutional pulling again if you want your own architecture copy this again at the end of this again Ctrl V again this is your other architecture so here I am using three strike of convolutional uh pooling convolutional pulling convolutional pulling is this clear okay guys so please go ahead of uh checking the jupyter notebook and you will find that in this jupyter notebook I have applied five Epoch to convolutional layer and at the end of five epic only I got 99 accuracy so convolutional layer is beating a n n at the epoch 5 only no need to go for 10 epoch no need to go for 10 Airport for the parameter calculation and all I I need a time but due to time limitation I cannot uh go ahead but in the next session I will meet no worry I I request the organizer to just give me the next session also so that I can explain in detail to them so thank you very much guys and that's all from my end due to the time limitation I cannot able to go ahead and uh soon we will meet okay just uh Sagar provide me one more uh what I can say session because participants want to learn that and uh thank you very much guys I hope that you understood majority concept although I understand I went fast okay and such a high level constant within one and half hour is a very difficult thank you very much whatever I explained like this was a great session and I think participants have enjoyed much more I can see in chat section uh we'll get back to you for another session yeah Sagar one thing I just want that does every time we are having limitation for one hour or can we go for three hours also uh I'll need to check it with uh my seniors too because this session was in our session we started okay so we need to see us uh that will be get back by the editor side yeah and uh all the participants you can connect meet the LinkedIn by the name arpit yadav and our designation is senior data scientist okay uh we'll share the uh our pets link uh LinkedIn profile you can uh following their uh if you are there please share the link for a bit profile or I'll just share it with you yeah my site so please share that okay so that participant can connect for their doubt and I can uh I will be very happy to solve their doubt yes computer vision is the part of data science world because obviously we need a data in the computer vision in terms of images and video in terms of images and video okay and uh other question swathi Pandey my profile is not I was not able to say you just send me connect request Swati connect request so that I can able to take that yes we can use CNN in Quantum Computing also but depends on what kind of problem statement we are solving there Quantum Computing is a very recent field what what can we get resources to do lot of computation example computational GPU for free okay you can go for uh actually uh computer system with the GPU processors if you are not having that use the cloud computing called AWS Amazon web service and so on can we say filter is trained with weighted filter Venus yes that is whatever filters we are applying right now uh it is given by the researcher whose values are fixed if you want to design your own it takes lots of time I am not saying that we cannot design yeah we can design that what are the different other question how to decide filter value as I said filter value we can decide by n number of research process can in pooling layer losing the information because in Max pooling we are ignoring no no we will not able to lose any information instead we are retaining the maximum information from the max pooling operation does the kernel always to be three by three no kernel size should be three by three five by five seven by seven depending on our need why do we use several convolutional layer because for the complex problems in the complex problem statement we are using several convolutional layer to extract the features initial level of convolutional here extracting the basic features information and the further level of convolutional layer is exactly extracting the problem statement kind of features pooling layer what is the difference between pooling and platon pooling layer is used to reduce the dimension flattening layer is used to convert 2D to 1D two dimensional to one dimensional what all pre-processing is mandatory for image in CNN you have you should have all the images of same size you can do the normalization of the images the N number of image processing you can do in that ritesh number of hidden layer is also hyper parameter yes number of hidden layers are hyper parameter transfer learning means what as I told you at the starting there is one competition called as imagenet and that imagenet competition has been organized for multi-class classification for thousand category the winners of that competition that architecture is declared as a transfer learning architecture state of art architecture that is a transfer learning we are using the predefined architecture for our problem statement which is matched with the transfer learning problem statement is this clear everyone so thanks everyone I hope that your doubt has been resolved is if if not please connect with me LinkedIn I will be happy to provide you more time to clear your doubts foreign so nice to interact with all the participants thank you very much guys I really enjoyed a lot by interacting with you thank you very much any question I am waiting again let me know so we have shared the arpegs LinkedIn profile in the chat section I'll share it again and you can uh go there and raise your questions and follow him on LinkedIn Financial