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Buy dissertation writing phrases buy a new kindle transfer books glamour essay [Music] so actually didn't learn both actually started if 1980s and many is many Niroula knowledge is they started studying the brain human brain and they they found this kind of architecture inside the brain that how brain learns how human brain learns thing and then some people who were in computer science they also read those things and started their making those things that the human brain was doing and started training the network so first I will explain water neuron in our brain walls and then I'll just move this analogy into our neural networks that we use in our training right in Python in all the libraries so so in in the human brain this is one kind of neuron right this is one and in in our brain there is not one there are kind of millions and billions of neurons which are trained once once we do anything and they keep learning all the time they give her out they give us output as well as keep learning like so this part this part is called a cell or a neuron we can call it and these are the dendrites so they pass the information from the cells so signal comes and go and these are the terminal verbs so these dendrites are connected to some other neuron right and they will give signal to this neuron this neuron will do something with this signal like some manipulation or some activation deactivation of the signal and that signal will be passed to the next terminals next bulbs which will be utilized or they will be passed to the next neuron so it will become a whole network of the neurons the which will be connected by their axons and then finally will go to the terminal bulbs and which gets the input from the dendrites in dendrites are connected to the some other inputs some other neuron so that the whole new network becomes and that is what is called neural networks right so similar thing if we apply in our model right think about this that this is a neuron a cell body this green right and it is connected to other neurons which are golden right so these branches are dendrite this is an exon okay this using this exon so using these dendrites this neuron is taking input from something somewhere right and this neuron is doing something inside that is doing something with these signals all signals are coming from different directions it does something and then whatever the output is it passes to some other neuron or some other terminal nodes of which we are calling terminal burrell and then it can be used in as an output or it can be used to the input as an input to other neuron as well right so instead of this output if we put if this is not an output if this is a neuron that will via another neuron taking input from another neuron so let's talk about this basic neuron right now so this is a basic neuron which is only one neuron these are called input puts right so because they come in to as a you know the layer so we call it input layer the the middle one is a neuron which takes the input do something with the input something and what we do what it does we'll talk about it in some in just some moments this is also called hidden layer and then finally it goes to one output right and then one output is called an output layer it can be one output or multiple output so that is the structure over one neuron okay this is one neuron it takes input there's something in between us is the output to the output layer and whatever the output is it is getting it now these W 1 W 2 W 3 these are the weights weights are applied to inputs so whatever X 1 is coming weight will be applied to that whatever X 2 is coming we'll be applying to that whatever X 3 is coming some way it will be applied to that and using these weights and the inputs neuron will do something with that and it will create some signal it will pass to the output that's the flow and the working often your own basic neuron now what what could another thing can happen that these neurons can be multiple right so something like this like so something so again these three inputs are coming right but we have multiple neurons see one two three four neurons we can have five even 10 or 100 neurons so this is one neuron right and we can have another layer as well so this is another layer right so one layer is this another layer is this and then at the incomes output layer so it will always be fixed will have one input layer at the end will have one output layer and in the middle will have number of neurons in one layer and we can have multiple layers we can have single layer also we can have multiple errors right that that we have to decide that how many neurons per layer we want to have and how many layers we want to have so if you this is called a multi-layer perceptron so this one is called a basic neuron or a perceptron this is the basic model of a perceptron and we'll make this one today and this is called a multi-layer perceptron because we have multiple hidden layers right input layer and output layer always takes they will always be there because if you don't have input you're not doing anything if you don't get output what are you doing right and this input and output hidden layers are the ones that we decide that I want to make our model and then the more number of neurons you have and the more number of layers you have the more number of weights will be coming so see here we had only three weights W 1 W 2 W 3 right only 3 input right only free weights input done but here we have this X 1 it's passing input to every neuron so you see this arrows going to everyone so every input is connected to every every neuron and from here also every input is connected to every neuron so it's kind of everyone goes to everything right and the more connection you have the more weights you have right and our problem is to find a network our problem is to find w1 w2 w3 so that we have the output that we want right so what we have done integration that we we used to have 1 number of inputs so and then we had one output and we were training a regression model such a way that our the prediction that the model is giving is as close as possible to the actual output that we had right the same the problem remains the same here also only the technique changes right and somehow this techniques works better than those regression models right so that's why again we have to make find out w1 w2 w3 and we have to find out those w1 w2 w3 these are also called the weights so I will be calling them weights so we have to find those set of weights which are optimum in a manner that the difference between actual and predicted is minimum uh note our goal is to make it zero but you can never achieve zero you will achieve you in some ideal cases but in real-world cases you will never achieve zero so in that case we have to find out the solution where the error like the difference between actual and predicted is minimum try to correlate with regression the same thing we are doing it here also but only thing is that our approach is changing there also we were trying to find some weight which we called intercept coefficient here also they are the weights we have to find out the optimal value of the weights so that the predicted values used by predicted values given by the model used after using those weights and the actual value the difference between them is minimum this x1 x2 x3 are inputs this is neuron this is output right this layer this because this whole thing we call it hidden right because why we call it it's always visible to us but to someone outside it's not visible like this thing like that but we call it a any layer any excuse but this is hidden layer we call it and we will be cutting okay so that's the thing now our objective is to reduce the error minimum Eider we have to change the weights we have to training now indeed this neuron this is doing nothing we I said that this neuron is doing something with the weights and x1 x2 input it is doing nothing it is just multiplying the weights X 1 W 1 X 2 W 2 X 3 double Utley and adding them so that's the formula for that so if you know even some basics symbols of math there's a summation of W ixi where I equals to 1 to M M here is 3 so these could be n also like 10 inputs also and if you talk about what is X 1 X 1 is nothing but your column X 1 so you had 10 features x1 x2 x3 extent so X 1 will always goes to go to this so value from X 1 like the first value from X 1 will go here first when you value from X 2 will go here first value from X 3 will go here way W 1 W 2 W 3 will apply this neuron will do all the sounds will pass it to the output again next row comes next row in your data comes second value of the X 1 second value of a second value of x3 will go which again will be applied they will be added by added in this neuron will be same hand to the output correct this is how this is working so each row comes all the related values for all the features go goes to the inputs weights applied summed up send back to the output this is called a feed-forward Network this is everything is going forward right and here you enjoyed learning from this video please like the video and if you have any doubts regarding this video please comment us in the comment section and don't forget to subscribe to our channel for more such informative videos to look out for other related videos in our playlist for more information visit our website now keep learning with intellibid how many hours does it take to write a dissertation American Academy McAllister Institute.