Hello guys this is Saksham Jain and I welcome you
all to The FutreCamp. So in this video we will be learning very briefly that what is artificial
neural network how do we get how did we get the name artificial neural network what is feed
forward artificial neural network and how is feed forward ann or artificial neural network different
from a regular artificial neural network so all these things will be covered in this brief video
so without wasting any time let's get started so neural network word has been derived from neurons
so you must be knowing that neurons are also called our brain cells and these brain cells or
neurons extend throughout our whole of the nervous system all these neural networks are connected to
each other and they communicate with the help of electrical impulses or signals so we know that our
brain is made up of neurons we also understand the working of these neurons how do these neurons work
so what we had tried to do is what the scientists tried to do is they tried to replicate these
neurons they tried to replicate the working of the of these neurons and since we have replicated
or i can say simulated this neural network this network of neurons artificially hence we got
the name artificial neural networks now let's talk about feed forward artificial neural network so
feed forward ann is a part of artificial neural network so that in the later stages when you will
be learning about other kinds of neural networks and many other complex neural networks when you
will later on learn about cnn's rns and every other neural network whatever you learn there
your base is going to be of artificial neural network especially of feed forward artificial
neural network so if you don't have this base if you don't know the concepts of ann you won't
be able to understand the concept of concepts of cnn and other neural networks in logistic
regression we feed the input to our neuron you must be knowing that in our logistic
regression we feed our input to our neuron and we get our output so here this is the working
of our logistic regulation the inputs goes to this head and we will get a prediction from this so
now let us suppose that i insert one more neuron now both these neurons this is this
is first neuron this is second neuron this is one neuron this thing is one
neuron now this is the second neuron now all these neurons get the same input but
they surge or i can say they compute different outputs so this neuron might be looking from uh
might be looking for some different features or can be computing some different values and this
neuron will be looking for some other features and might be computer computing
some other value now let us suppose i insert one more neural neuron here and so
on i am able to connect a network of neurons now since there are many neurons or i
can say many logistic regression neurons so i can say that this is similar to
simply multi-class logistic regression here the work of each neuron is to compute a
different feature or to compute a different output or we can say that to look for different features
and here you can see that i am feeding some input and i will be receiving some output from the right
hand side i will be feeding input from the right uh left hand side and i will be receiving and i
will be receiving output from the right hand side since this is going towards this direction
forward direction i feed the input from the left and i receive the output in the right so uh hence
i got the name as feed forward neural network in feed forward neural networks there is no looping
between the neurons you know that in brain all the neural neurons are connected to each other in form
of loops in forward direction backward direction every direction they are connected to each other
so but in feed forward neural network they are connected in only one direction that is forward
from left to right here you can see that all these all the neurons are connected to each other they
are forming kinds of loops they're forming loops in the nodes so feed forward neural network
won't look like this it will some it will somewhat look like this so this is a perfect
example of feed forward neural network here i feed the input from the left hand side and i
will receive the output from the right hand side so this is the difference between artificial
neural network and feed forward neural network in artificial neural network what i do is i have
tried to simulate the working of a brain feed forward neural network is a part of artificial
neural network that goes from the where the neurons go from left direction to the
right that is in the forward direction so that is the difference between food for neural
network and artificial neural network so that was all for today guys i know that this video was very
brief but uh believe me in the upcoming videos we will be covering each and every part of artificial
neural network that is forward propagation loss functions optimizers back propagation
hyper parameter tuning and many other things we will be covering each and every topic in in
a much detailed way so let us meet in the next video in the next video we will be discussing
our learning path our roadmap for artificial neural networks particularly feed forward enns so
let us meet in the next video until then bye bye
1) Artificial Neural Networks (ANNs) & Feedforward Neural Networks
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