ANN CNN RNN Flashcards
what is ANN
artificial neural network
explain ANN
An Artificial neural network is network based on biological neural networks that construct the structure of the human brain. Similar to a human brain has neurons interconnected to each other, artificial neural networks also have neurons that are linked to each other in various layers of the networks. These neurons are known as nodes.
what are nodes
Similar to the human brain that has neurons interconnected to one another, artificial neural networks also have neurons that are interconnected to one another in various layers of the networks.
These neurons are known as nodes.
what is the architecture of ANN
Lets us look at various types of layers available in an artificial neural network. Artificial Neural Network primarily consists of three layers:
Input layer
Hidden layer
Output Layer
what is input layer
it accepts inputs in several different formats provided by the programmer.
what is hidden layer
The hidden layer presents in-between input and output layers. It performs all the calculations to find hidden features and patterns.
what is output layer
The input goes through a series of transformations using the hidden layer, which finally results in output that is conveyed using this layer.
write diff between ANN and BNN
from PDF
what is single layer feed forward neural network
layer is formed by taking processing element and combining it with other processing element
input and output are linked with each other
inputs are connected to the processing nodes with various weights resulting series of output one per node
zero hidden layers present
what is multi layer feed forward neural network
it is formed by interconnection of several layers
there are multiple layers between input and output layers known as hidden layers
input layers receives input and buffers input signal, output layer generates output
zero to several hidden layers present
what is CNN
convolutional neural networks
explain CNN
neural networks that use convolution in place of general matrix multiplication in at least one of their layers
