#23 Introduction to Artificial Neural Networks & their Representation of Neural Networks |ML|

[Music] hello everyone welcome back to my youtube channel trouble free in today's video i am going to explain about the introduction to artificial neural networks and also the representation of artificial neural networks in the subject of machine learning okay so first we will learn what neural network is what a neuron is right and what is an artificial neuron and then we will see how to represent that what are the different parts we have in that and all okay so first and one more thing before starting video let me tell you something if you are having your exam schedule nearby just let me know the date of your exam along with your college name in the comment section so that i'll make videos by that time i'll try to make videos by that time and according to your college syllabus okay done so let's get started now first our human brain has so many neurons right if you have studied in your 10th class in biology we will have the concept of not only 10th in 8th 9th also we will have the concept of neuron right so neuron nephron we will have right nephron is something which is related to kidney now neuron is something which is related to our brain right so in our human brain in our brain inside our brain there are millions and millions of neurons neuron is a very small unit right and there are millions of neurons in our brain you cannot see neuron with your direct eye right so in biological terms so wait before that what neurons are they are the very smallest units and they what they will do they will do the sensory tasks so whatever sense organs are there in our body like we have eyes no skin and all right we have five sense organs right so all the sensory tasks like you know when you're touching a hot bowl you need to take off your hand or when you are stepping into um floor or a place where you have all thorns and all you need to step back right so like that you will have if if there is something fire or something you need to come out of there you need to you should not go there so all those sensory organs you are which you are doing are nothing but they are stimulated by your brain and which organism or which organ will help neurons will help in stimulating the response okay i'll tell you with example don't worry so the natural functionalities the natural tasks which are done by our brain are done artificially and that is called as artificial neural networks and what are those artificial neurons they are nothing but nodes in biological terms we have neurons right in formal terms we have nodes in artificial terms that is called as node okay done so for example when you are touching a hot vessel what happens your your your hand will burn or if it if the vessel is very hot then obviously your skin you know you'll get some bubbles on your skin your skin becomes red on all right so all neurons which are related to your skin or your hand or whatever all neurons will get activated and they will send that to brain that your body or your skin has touched something very hot right and what brain will do brain will collect the input from all the neurons right all the neurons input the brain will take and it will process and generate an output what is that output to take back our hand and all these happens in fraction of seconds again you can get a doubt uh whenever we touch we immediately take back our hand then how this all process will happen all this process will happen in fraction of seconds okay so this is how neurons will work so in the same way nodes also will do the same thing they will be taking all the input the in the using some function it will be processed and you will be getting the output okay done so the same process happens as i said so now let us see um with with diagrammatic representation let us see now this is a node okay this is a node and a node has two paths this is summation part and this is the activation function two parts one is summation and the second one is activation function okay now x x1 x2 x3 xn and so on up to xn are nothing but they are the input signals okay x1 x2 x3 xn and so on they are the input signals and what are this w1 w2 w3 wn they are the weight corresponding weights of these input signals this will indicate the you know importance or the weightage you can say okay each and the weight of the corresponding input symbols okay done so now what happens when all these will go inside as you have summation function and you also have activation you have to write and then output will be generated which is a function of both x i and w i ok done so see it has two functions as i already said one is summation and the other one is activation so in summation what happens is it will calculate the weighted sum of all the inputs weighted sum is nothing but x1 w1 x1 along with its weight plus x2 w2 which is nothing but sigma x i wi okay the weighted sum of all the inputs x1 w1 x2 w2 the weighted their corresponding weight and the input multiplied together okay so once you get the weighted sum that you need to send into the activation function so once you are done with calculation of this sigma which is nothing but the weighted sum you need to send it to the activation function activation function is nothing but it is denoted by f and what this activation function will do it will generate the output based on the input which we are given what is the input that we are giving to the activation function the weighted sum wa xa or xaware so when we give this based on this it will generate an input as i said the brain will collect the input from all the neurons so here also it will collect the input from all the inputs some all the inputs sum together will be given as a combined input and then you will be getting the respective output okay so this is how it will work then are you clear now so a node has two paths node is nothing but it is an artificial neuron so it has two parts one is summation and the second one is the activation function done so now let us see the representation of these artificial neural networks how do you represent it the artificial networks uh neural networks are actually divided into three parts one is input layer two is the hidden layer and third is the output layer i will show you i will show this to you with an exam with a diagram so that you can understand it more better right and what input layer will do what hidden layer will do and what output layer will do individually that also we will learn now so yeah if you see this diagram you can understand this is input layer this is the hidden layer and this is the output layer okay and you can have any number of nodes in each and every layer it's not like only two only three only two you can have any number of nodes based on the input that you're taking okay done and here each and every node has to be connected to its you know input layer is connected to hidden layer and hidden layer is connected to output layer so how these connections are each and every node of this layer should be connected to the each and every node in the next layer for example if you can see this this node is connected to here here and here and this also here this and this right and if you take this also one and two one and two this also one and two so one this layers nodes all the all the nodes of this layer should be connected to all the nodes of this layer and all the nodes of this layer should be connected to all the nodes of the next layer like that okay and what each and every layer will do let us see now so first what is the input layer right so input layer will receive the input layer will receive the input signals okay i'm sorry okay input layer will receive the input signals or data or information or whatever it is it will receive and then it will send to the hidden layer what hidden layer will do hidden layer has neurons which are responsible for extracting the information from the input whatever the input you have got you are extracting some information from that you know the relevant data or the important data is taken from that input and that data is processed and an output is generated done all this will happen in the hidden layer so the hidden layer is very important layer where all the data processing all the output generation will takes place done and the generated output will be sent here and what this will do it is responsible for producing for producing and also the presenting the output to the user okay so first is input layer input layer is responsible for receiving the inputs or signals or data and based on the input the input layer has received the hidden layer will process that information and generate the output and that output is sent to the output layer output layer will produce or it will present that output to the user right so this is what actually happens in in a your you know in artificial neural network so you can represent an artificial neural network by using three layers the input layer the hidden layer and the output layer okay done so that's all for this video in the next video i am going to explain about the appropriate problems for neural network learning so till then stay tuned to my channel and if you're still having any doubts apart from what i've explained in this video you can ask me i'll definitely try to reply to all your comments for sure and if i cannot reply for any of the comment excuse me for that i'll definitely maximum i will try to reply for all the comments and that's all for this video let's meet up soon in the next coming video with another topic and if you want me to make any other videos or any other topics just let me know that in the comment section i'll definitely make videos for you people for sure okay thanks for watching the video let's meet up soon the next coming video with another topic

Channel: Trouble- Free

Views: 13,003

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Id: WtUfElRD3rU

Length: 10min 17sec (617 seconds)

Published: Mon Aug 09 2021