I read about an issue like, why are new roads are really cool?
We cannot just use a single neuron to perform complex has.
This is the reason our brain had billions of neurons stacked in layers forming a network.
Similarly, artificial neurons are engaged in layers.
Each and every layer will be connected in such a way that information is bought from one to another.
The typical Annand consists of the following layers input layer upon layer and about layer.
So Layer has a collection of neurons and are neurons in one layer interact.
With all the new rules in other areas, however, new rules in the assembly will not interact with one
another simply because new rules from the adjacent layers have connection between the however new rules
in the same place.
You do not have any connections with your thumb, nor are you to present the new rules in the artificial
neural network.
And a typical animation in this.
As can you say, that in both hit and light and how put your shoulder input layer is where we fit in
to the network, the numbers up newroz it is the number of in both we feed to the network.
Each input will have some influence on predicting the output.
However, no computation is performed in the input layer.
It is just you for passive information from the outside world to the network.
He didn't lie any lie between the input layer and the output layer is called a layer is processed.
The input received from the input layer, the hidden layer is responsible for the arriving complex relationships
between input and output.
That is, the hidden layer identified.
A pattern in the data set is a majorly responsible for learning the data representation.
And for extracting the features.
There can be any number of years, however, we have to to a number of Hidalgo's, according to our
use case.
For a very simple problem, we can just use one dollar, but while performing complex task such as image
recognition.
With so many hits like yours where failure is responsible for extracting important features, the network
is Konar deep neural network when we have many hit delayers.
About layer after processing the input, the hidden layers and its result to the output layer and the
name suggests I like it missed the output, the number of neurons in the output layer is based on the
tile problem.
We want our network to solve it.
If it's a binary classification, then a number of new roles in the Abulafia is one that tell us which
class in Bush belong.
It is a Montebourg multiclass classification, say, with five glasses, and we want to get the probability
of a glass as an output than the number of new in the output layer is five ish, emitting the probability
it is a regression problem, then we have one neuron in the Abulafia and that is on about and I hope
you enjoy it and I will see you in the next video.