we talked about artificial neuron, Bias and activation function in last videos
and now we can conclude that neuron is the fundamental element of Neural Net
 
and when we train the a neural network we want the neurons to fire
whenever they learn specific patterns from the data
and we model the fire rate using an activation function
 
this is the typical structure of the neural net
these arrows shows the inputs
and these color dots shows
the neuron
before starting feedforward, i would like to  introduce some basic terminologies
related to neural networks
first of all , Input Layer
Hidden Layer
and Output layer
Input nodes or input layer
we combine the nodes as a layer
in neural network just for convenience sake
No computation is performed in input layer
that layer passes input as it is to output layer or hidden layer
Hidden Layer
These are the most important layers of a neural net
there may be arbitrary number of layers , Because number of layers depends on the application
 
this is where computation and processing  is performed
it is possible to have a neural network without a hidden layer
Output Layer
This layer uses an activation function to map input to the desired result
simply
this layer is responsible for Output, that we get
Types of Neural Network
there are so many types of Neural nets such as
feed forward
recurrent
convolutional
U-net et cetra
but feed forward neural network is the main topic right now
Now, why is it called "Feed Forward" ?
because the flow of information or input is in just forward direction
it means ,  there can not be any loop in this network
we'll talk about loop and backward propagation
in our later videos
this is why , it is known as feed forward Neural network
 
There are two types of feed forward Neural nets
first, single layer perceptron
this neural network does not contain any hidden layer, it just contains a single layer of output nodes
Multi layer perceptron
this is what we use more frequently
there is a very little difference between multi layer and single layer perceptron
single layer perceptron  desn't contain any hidden layer. though
 
multi layer  may have one or more hidden layers
Now, some important things to note
we don't count input layer while counting layers
we use
such as sigmoid , tanh,  relu etc
in hidden layers
we'll see these activation functions in  later videos
we use
softmax for classification and linear for regression
