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In the last lecture, we saw how to create, how to compile and how to screen our classification modern

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intensive, locate us.

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In this video, we will be and crane a deflation model using get us.

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For this, we will be using the very popular regression dataset that is California housing data set.

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This dataset is available in a Skillern database library.

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The objective here is to predict the prices of homes using eight different independent variables.

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So let's get started.

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First, we are importing some basic liabilities, such as by undoes and my plot.

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Then we are importing tents up, flowing gave us.

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And then since this data is available in Skillern dataset, we are also importing California housing

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from Escalon big.

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And we are saving this database and doing another really well called housing.

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I also want to share one small shortcut to access the help of any function.

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So if you just click inbetween of any function parenthesis and then hold the ship key and click on tab.

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So if you hit ship less there, it will open the help or documentation of that function.

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If you hit shift 10 two times, it will expand the documentation.

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You can see here this function will return.

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Different parameters such as not data.

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It will give us the independent variables.

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Dot, dot, dot will give us the dependent variable.

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And the feature entity will give us the details of features.

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Let's just close this.

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So if you have any doubt regarding any function, just click between Prentice's and her shift plus.

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So I have alluded this set some of the information about this database.

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So in this database, there are around 20000 records.

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There are eight independent variables.

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We have first variable as made in.

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This is the medium income in that particular block.

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Warehouses located.

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Then we have a second variable that is house eight.

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This is the median house eight in that block.

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Then we have average rooms, which is the average number of rooms.

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And then we have average bedrooms for average number of bedrooms.

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Next, we have population variable.

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That is the block population.

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Then we have average occupancy.

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That is the average house occupancy.

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And then latitude and longitude of that pole's block using this eight independent variables.

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We want to predict the value of the house.

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The values are in a hundred thousand.

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So supposing forward, why would you build these five?

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That means the value of their houses.

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Five hundred thousand dollars.

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So this is our dataset.

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We have this eight independent variable and one target variable as space.

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If you want some more detail about this dataset, you can click on this documentation link.

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This will open the official Escalon documentation of this dataset here.

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You will get to know about all the parameters that you can give.

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And what are included in this database?

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Let's go back.

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So this housing is in the form of a dictionary.

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We have one you loopier as feature me.

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So just look at the feature names first.

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You can see these are the eight variable names that we have discussed already.

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Now to EXs, the independent data we have to use housing data and to access the independent data set.

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We have to use housing target.

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So in this line of code, we are splitting our data first into green full and best dataset.

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Then we add further dividing this green full dataset in to extreme and X valuation dataset.

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We will be using test train straight from Escalon model selection, and then we will use test train,

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the split method to divide our data.

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We are not giving any additional parameter for test sites.

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That's because by default, the test size is 25 percent of the total data.

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So 25 percent of the total data that is around 20000 goes will go into test set.

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And then the remaining 75 percent will go in to training site.

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All of that 75 percent.

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Again, 25 percent will go into validation set and less of 75 percent will go into a working site.

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Let's run this.

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Next step is to process our data and we will be using the standard is scalar from Escala to standardize

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our data.

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And standardizing, we subtract the mean of each variable from their individual values.

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And then we also divide it by the variance because at the end we want all the variables with mean as

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zero and their variance as one.

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This is a standard procedure to create any machine learning more than the steps here are very simple.

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First, we are importing standard scale it from a skill pre processing.

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Then we are creating the scalar object using standard scalar method.

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Then we are training.

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This is Escala Object using Nowhat Extreme Data.

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So on our extra data, this is killer will find the values to subtract, as I mean, and to be weighed

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as a variance.

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Then we will use those values.

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Or this is scalar object to standardize a word validation and assets as well.

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Just so repeat, we are fitting.

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This is scalar object on our training data.

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And we are transforming our validation and test set using.

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This is scalar object that we have put it on our extreme data.

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Fate will be using fate, underscore, transformed, mattered.

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And we will be using extreme as parameter to transform.

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We will be using dort transform method of this scalar object and we will be using relevant datasets

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here.

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So let's create a standardized datasets.

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We are saving this object in their original name only.

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So we are replacing the ordinal extreme with the standardized version of extreme.

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Is regional ex validation set in to a standardized version of X validation set?

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And same for this site as what it says.

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And this if you want some more information on this riskily.

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You can always do for two and Skillern documentation.

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More standard is scalars.

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Next step is to set random seeds.

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This is to generate the same result every time we run this modern.

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Now, as I said earlier, our initial data set was off or on point, deposing laws or records.

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Now let us see the shape of the extended SEC.

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Here you can see we have eight columns and around eleven thousand six hundred reports.

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You know what?

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Extremely desolate.

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We should never own five Posehn records in our X test dataset and around 4000 in valuation, say.

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Now, let's create the structure for over regulation neural network.

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Here we will be first having an input layer.

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Then we will be having the first dance layer with party neurons.

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Then we want to create second dance layer with another turbin neurons.

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And then since this is the immigration problem, we will be having a single output neuron without any

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activation function.

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A single neuron, since we want a continuous value as our output.

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Again, we will be using the sequential EPA.

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We are saving this structure or our model as model.

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And then for the first, Leot.

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We are writing.

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Get us dot leered, dot dense.

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Here in parenthesis, we have to provide the number of neurons, which is starty.

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Then, as discussed in our three lectures, we will be using activation function as the RELU.

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And then since this is our first Soudan there, we need to provide the input shape since the number

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of independent variables in our data is eight.

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We will be using input chip equal to.

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You can also use like this input shape.

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Do extreme dort shape and then calling the second and so on, elements of over input chip.

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This way you don't have to worry about changing this number every time you change your data base.

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You can just straight xstream not shift and it will automatically get the number of variables from the

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shape attribute of forward extreme object.

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So this is the structure of our first dance layer will be cleared.

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Second, dense layer in a similar fashion.

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We will be using give us dot layer, dot dense.

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And then the number of neurons in the parenthesis, which is 30.

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And activation function as the RELU.

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Similarly, for the output layer, we will be using guitars, dot layard's dance.

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And since this aggression problem, we will be using a single neuron without any activation function.

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Just run this.

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And again, one important thing you can comment using hash symbol inside the cells.

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So Biton will execute on this part of the code and will not be executing any code which starts with

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Peche as planned for starting the coming.

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So now we have created the structure or architecture of what neural network does to conform and view

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this structure.

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We can call thought somebody might just write Mordred or.

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Somebody here, you will get the information about the structure that we have created.

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So we have first dense layer with dirt in Iran.

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We have second dense, layered with protein neurons.

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And lastly, we have a single output layer with one neuron.

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This is what we wanted.

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The next step should be to come find this Martin.

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Again, the combined method works similar for both classification and duration model.

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First, we have to mention the loss in classification.

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We were using Crosson Groppi.

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But Pia, since we are planning regulation, we have to use mean squared error, also known as MASC.

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The second parameter is optimizer.

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Again, here also we are using as Sudi, so plastic gradient descent.

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And here we are also providing the learning rate by default, the value of learning rated zero point

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zero one and two genes.

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You can just write.

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The new value in the parents is.

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We have already discussed what is learning English in order to re lecture.

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So if you have any doubts, just revisit that lecture.

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And then the next parameter that we are passing is metrics.

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This is an optional parameter.

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In classification, we were using accuracy.

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But in regulation, we can use mean absolute error or m a e absolute error is the difference between

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the predicted value and the actual value.

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Whereas the mean is squared error is the square of that difference.

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So we are concluding both mean squared error as a lost function and Emmy as the metrics we additionally

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want to calculate.

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Again, just remember, if you want to look at the documentation or hell, just click inside any of

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the parenthesis and.

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And press shiftless tab, you will get this kind of documentation.

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And here you can see that by default the learning rate value is 0.01.

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So in classification, we did not provided any learning rate.

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So the learning rate that was used, there was zero point zero one.

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But you can always teach these values according to your need.

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Let's run this.

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So we have compiled over more than.

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The next step, the next step is to screen out more than using screening data.

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The method or the process is same.

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We are creating another object model underscoring speed for screening.

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Then we are using more than not fit and we are passing our training dataset number of box and the validation

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dataset that we have created.

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Just run this a statement.

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Again.

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Just like the classification model, you will get the lost value, which is the squared error.

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You will get the Emmy you and you mean absolute terror.

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And similarly, you will get these two values for your validation set.

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That's when.

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And you can see that the loss on both training set and relevation set is decreasing with each epoch.

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Now we have these values for our training and validation set.

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We can also evaluate performance of this train model on our test set.

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And we are going to use the same method as fitted with classification model.

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We'll call model DOT evaluate.

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And then we will pass our training test.

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Then this.

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You can see on our trained deselect loss is zero point three.

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That is masc or squared added and m e e mean absolute error is zero point four four nine three.

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Now, just like in classification, we can call modern history, not history, that will give us the

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values of all this much crisis in the form of dictionary.

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And this year you will get the loss and Emmy on dreaming.

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Does it end?

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And Relevation Lowson validation me.

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The beauty of this is we can load this dictionary on a block just like we did for classification, and

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that will show us how we're training.

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Loss and reputation loss are changing with each epoch and whether we have achieved the convergence or

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not.

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This and this.

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So you can see we have the loss values and the Emmy win lose photo, well, training and validation

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set out on this graph.

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And one thing to notice is this graph is still going down, meaning that if we won some more epochs,

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this will further decrease the losses and improve the accuracy of our THAN.

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So this is the one way to tell whether you have achieved convergence or not or whether you have to increase

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your epoch relative or not.

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You have to look at this validation, loss and validation Emmy value.

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So this the validation loss.

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And you can clearly see that it is going down.

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So to improve the accuracy, we can read on this called.

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Run it for finding more Reeboks.

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So just go there.

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Now, one important thing about guitars, these guitars have the beats and Bias's value in the memory.

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So if you just redone this whole this statement, again, this will not screen the data from Esack.

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But it will start training the data from this position.

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So if we run this a statement two times that is similar to running a statement with the box.

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If we just read on there one more time.

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You can see earlier the lost values were.

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Around point seven or eight point two for the first Sepo.

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And then gradually decreasing.

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But now we have started from that 20 plus epoch.

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Last same, the lost value on our test set was zero open three zero.

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Let's see whether we have improved this lost value or not.

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You can see the losses decrease from zero point three to zero point two five.

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So what have put this is was correct that the model was not converged in 2012.

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There was room of improvement and B, B, then the whole more than 420 more epochs.

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That is a total of four epochs.

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And we can see this graph.

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You can just.

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Focus on this reputation loss lane.

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Earlier, it was set upon for a long period and this.

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There is a slight decrease in valuation loss.

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Now you can see that the line is flat turned out.

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This means we have achieved the convergence on this model.

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So not just with regulation.

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If you are running classification model as well.

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Just look at this graph to identify whether you have achieved convergence or not.

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Now, to predict the values on the new dataset, you can always use, not predict metric.

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So your object name and dot predict method.

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And then the new dataset.

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I don't have any new dataset.

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So I'm just creating the sample of those three values of my X dataset.

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And considering it as my new dataset and then saving the information in Y pretty good values and using

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model lot.

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Predict my turn to predict the values that the Fed will use using this model.

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That's all for dyslectic in the next lecture.

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We will be looking at the functional EPA of get us.

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Thank you.