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Now that means that any kind of data that we're putting in the bit images or audio or text or something like this. This all needs to be converted into arrays. This is easy enough. As the digital media companies in Mumbai, If we're just working with simple numerical models but for example, stuff like a text prediction model might take in an array of text and we would have to divide that array of text. It's a small array of the individual strings that might represent single words. Similarly if with feeding and stuff like image data well then we need to convert the image data into basically these arrays or maybe pixel values or R-Ga. values or something like this. Either way, the data we see didn't need to be in TENS of format.

And this can be a lot of the challenge is trying to find a clever way to convert your data into a type input into which we can feed or other and that we can feed into our model as we are the best digital media companies in Mumbai. Now as I just mentioned building a program intensive flow can be divided into two main sections or two main tasks. The first is going to be building up our computation of graph using nodes and a bunch of variables and values. And the second part is going to be running out will crowd be it to train the model to assess the validity or accuracy of the model or to actually computes and predict something.

So building the graph or the computational model it solves is in my opinion actually the hardest part about this. And the reason it's so difficult is that it's a pretty finicky process. There's a lot of different parameters to take into consideration and finding the optimal model to use as prediction can be a bit Knauss in an office sells for digital media companies in Mumbai. This is unfortunately just something that comes with practice and experience using tens of flow and the most optimal model is very often not clear right off the bat. However, all I can do is teach you the basics provide your few examples and then it's up to you guys to try to find the optimal solution to these problems because we are the best digital media companies in Mumbai. But the first step to building our computational model or our computational graph and actually by the way as an aside when I say Graaff I don't mean kind of like an x and y-axis kind of graph. I mean a graph that builds based on interconnected nodes.

So if you're not sure what that then searches up something like computer science graph and you'll get the general idea from there. So it was over saying our computational model is built entirely around a graph of nodes. So each of these nodes is going to take or more tensors or arrays as inputs. So again the first step is always going to be to convert our data that was feeding in into some kind of attends a forum what will need to use various types of nodes to build up our computational model as well will need to specify parameters such as the model type that we want to use. In best digital media companies in Mumbai, I think to keep things simple and because it's one of the simplest models anyway we're going to start off with a linear regression but we won't explore that in greater detail until a few seconds from now. We'll also want to specify stuff like a series of input parameters input values and then the expected output that we'd get. That would correspond with these as digital media companies in Mumbai.

So, for example, let's say we had a building kind of like an image recognition model. Maybe that was a feat in some images of like a boates cats and a rabbit or something and then the expected outputs would be the words that would represent those. So the inputs would be the images themselves obviously converted into intensive form and then the output would be the words that would represent those images as well. As the best digital media companies in Mumbai, We'll need to specify which promises we want to optimize within our model. So for example with a simple linear regression model which is basically y equals x plus B the input values would be our x values the output values would be the y values and the promises we want our model to optimize would be our m or our slope and B which is our y-intercept and then after this once we've built our model as basically across all nodes we'll probably want some kind of data to train our model as well as some data to test our model. Now generally speaking we want to divide our total data set into about a split. And I mean Acey said percent.

So percent is going to be training data and percent is going to be the testing data. Now the difference between the two is that the training data is obviously as its name might imply going to be used to train our model. So, for example, we'd see it in some images off again like a boates a cat and a rabbit and we want out we want to train our model in such a way that it will output those values then we'll, of course, want some testing data which is different from our training data. And this is going to be used to assess how accurate our model is. So if at the end of the day our model is very inaccurate. As a social media agency, we might want to either adjust the model promises or speed in some more data and train it further. So as you can see there's a lot that goes into building this model and there's a lot that can change about it which is why building this model can be so difficult sometimes you want a model that's not just accurate but also efficient as well.