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Here is a summary table of a classification neural network architecture.

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In the second table, you can see I have put four columns, first column in both the first table and

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the second table is for hyper barometer's.

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These are the values that we have to set prior to training our model.

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For example, how many layers will add neural network have is something we have to decide and give beforehand.

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So the common classification neural network hyper parameters are mentioned in the first row of table

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one and two.

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The second, third and fourth column in the second table are for three classifications, and I use flusters

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binary classification, which is classifying into two classes like marking a mail as spam or not spam.

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Second is multilabel binary classification, which means there are multiple binary variables.

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For example, the first variable is whether a mail is spam or not.

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And the second variable could be whether a mail is important or not.

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The third one is multiclass classification, which we discussed in last lecture.

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Also, if we have fought losses, trousers, shirts, socks and ties.

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This scenario falls under multiclass classification.

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Now, let's see what values of hyper parameters do we usually use for these three types of classification

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scenarios?

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The first parameter is number of input neurons.

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Number of input neurons are always equal to the number of input features.

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So if you have 16 input variables, you will have 16 input neurons.

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So you'll always have one neuron.

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But input feature.

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The second hyper parameter is how many layers do we want in our network?

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Ideally, this depends on the problem.

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But typically we keep the number of the layers between one, two, five, keeping more than five and

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a lid only increase the computational effort for our system.

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Third type of barometer is hidden activation.

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That is the activation function that we put on the neurons in decoding layers.

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This is usually RELU.

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We discussed rectifier linear unit.

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It is a very common function which is used for hidden layer activation.

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I told you earlier, also we used a loop because it is very fast to execute in our systems.

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Other three paper parameters very with the type of classification that we are doing.

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So the number of output neurons in binary classification is one.

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But then multilabel binary classification, it is one bad label.

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For example, if we are classifying an email as spam or not spam and the other label is important or

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not important, we will need to neutrons in the output layer.

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One neuron would tell us whether it is spam or not spam.

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And the other neuron will tell us whether it is important or not important for multiclass classification.

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We have one output neuron Berglas.

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So for example, if we are classifying images and two shirts, trousers, socks and ties, we will have

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four different output neurons for each of these glass.

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And we will put a softmax activation layer on top of it to get the probability of each class happening

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next, hyper parameterize output, layer activation in binary classification, logistic or sigmoid function

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works very well.

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You can use step function also.

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But as we have discussed, logistic function performs much better than a step function.

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So for binary classification and multilabel binary classification, we use the sigmoid function.

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But in multiclass classification, after the sigmoid function, we have to put an additional layer of

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softmax activation lost.

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Her output parameter is lost function.

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We will be using cross entropy as the lost function for all types of classifications.

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So these are the hyper parameters that you have to mention when you are running MUTILATOR work model

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in the software.

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The values that are given here are typical values.

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That is, these are commonly used values, but it is not a hard and fast rule to use these values.

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Only you can customize your neural network by using any other hyper barometer value.

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Next is the somebody, David, of the aggression neural network architecture head on the left.

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We have hyper barometer's and on the right we have typical values that we use for this hyper barometer's.

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The first one is number of input neurons.

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Again, it is one, but input feature number of lives, that depends on the problem.

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But usually we keep one, two, five hidden lives.

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Then comes number of neutrons, but he did it again, this depends on the problem.

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But typically we take 10, 200 neurons, indicate a list then as output neurons.

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If we are predicting only one thing, we need only one, not put neuron.

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If we are predicting multiple things, we need one output neuron.

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But the number of things that we want to predict.

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For example, if you are predicting house price, that requires only one or per neuron.

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On the other hand, if you are predicting the length and weight of a pet, I love a lot from the image

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of the flood that requires to output neurons one for the length of the petal and ticking for David totipotent.

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Next, US hit an activation hyper parameter, which means what will be the activation function independently

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as most commonly used activation function is Relu indicating layers relu cannot be used as activation

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function and output layer.

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So for regression neural network in the output activation, we do not really need any activation function

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as such.

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If you want to play any particular boundary condition on the output, for example, if you want that

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the output should only be positive.

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Then you can play a RELU kind of function on top of it.

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Otherwise, there is no requirement of an activation function on the output limit.

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Last iBOT barometer is lost function for regression neural network.

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The squared error can also work very well as a lost function.

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You cannot use cross entropy here.

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So we often use means squared error, which is the mean of squared errors that we calculate for individual

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training examples.

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So these are all the hyper parameters that you need to specify.

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While running a regression neural network in the software on the right, you'll see the typical values.

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These are not fixed.

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You can still customize your neural network by changing these hyper parameter values.

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That's all.

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See you in the practical actives.