Welcome back.
In this video we're going to talk about TensorFlow implementation.
I want from you to focus with me because those are the blueprint for our application.
Our linear relationship is price equals to three times size plus two plus noise.
Here we have the size as an input.
For example 1500ft².
We have the input layer.
The input layer takes only one number.
So this is a very simple model.
Other models takes many many numbers and many inputs like number of rooms like that.
The position.
But here in this example we are taking only the size in square feet as an example.
Then we pass it in the for the other layer, which is containing only one neuron dense with one neuron.
And here we have the equation y equal to w times x plus b slope times x variable plus the noise and
or the intercept.
Okay.
Then after this layer we have the output layer.
We need to get the price.
So this price will lead after the that calculations and the machine learning and algorithms, we get
the price $6,500.
Okay, so this will create a linear stack of layers.
Layers are executed in order from input to output, like stacking Lego blocks.
And creating those layers is done by the Keras dot sequential and that TensorFlow Keras dot sequential
function.
Also to make the layer dense.
Dense layer.
Fully connected layer.
Every input connects to every output.
So one number of neurons in the layer.
Here we have only one neuron okay one number of neurons in the layer which is the output dimension.
We have to get the price.
For linear regression one neuron equals to one output value.
So here since we are dealing with linear relationship linear regression one neuron equals to one output
value the predicted price.
So here we are expecting one feature per sample.