2. Key Points
covered in this presentation
What is Neural Network ?
Why we use ?
Neural Network v/s Conventional Computers
Interlink between Artificial and Biological NN
Types of Neural Network
Architecture & Working of Neural Network
3. Artificial Neural Network (ANN) is an information processing paradigm that is inspired by biological
nervous systems. It is composed of a large number of highly interconnected processing elements
called neurons. An ANN is configured for some specific applications, such as pattern recognition , data
Neural networks are ideally suited to help people in solving complex problems in real-life situations.
We can learn and model the relationships between inputs and outputs that are nonlinear and complex
, make generalizations and inferences , reveal hidden relationships , patterns and predictions ; and
model highly volatile data (such as financial time series data) and variances needed to predict rare
events (such as fraud detection).
What is Neural Network ?
Why we use ?
Neural Network v/s Conventional Computers
Conventional computers use an algorithmic approach, but neural networks works similar to human
brain and learns by example.
4. All mammalian brains consist of interconnected
neurons that transmit electrochemical signals.
Neurons have several components: the body,
which includes a nucleus and dendrites; axons,
which connect to other cells; and axon terminals
or synapses, which transmit information or
stimuli from one neuron to another. Combined,
this unit carries out communication and
integration functions in the nervous system. The
human brain has a massive number of
processing units (86 billion neurons) that enable
the performance of highly complex functions.
How the Biological Model of Neural Network Functions ?
5. ANNs are statistical models designed to adapt and self-
program by using learning algorithms in order to
understand and sort out concepts, images, and
photographs. For processors to do their work,
developers arrange them in layers that operate in
parallel. The input layer is analogous to the dendrites in
the human brain’s neural network. The hidden layer is
comparable to the cell body and sits between the input
layer and output layer (which is akin to the synaptic
outputs in the brain). The hidden layer is where artificial
neurons take in a set of inputs based on synaptic
weight, which is the amplitude or strength of a
connection between nodes. These weighted inputs
generate an output through a transfer function to the
How the Artificial Neural Network Functions ?
6. Types of Neural Networks :
Feedforward Neural Network
The feedforward neural network is one of the most basic artificial neural networks. In this
ANN, the data or the input provided ravels in a single direction. It enters into the ANN
through the input layer and exits through the output layer while hidden layers may or may
not exist. So the feedforward neural network has a front propagated wave only and
usually does not have backpropagation.
Recurrent Neural Network
The Recurrent Neural Network saves the output of a layer and feeds this output
back to the input to better predict the outcome of the layer. The first layer in the
RNN is quite similar to the feed-forward neural network and the recurrent neural
network starts once the output of the first layer is computed. After this layer,
each unit will remember some information from the previous step so that it can
act as a memory cell in performing computations.
7. Convolutional Neural Network
A Convolutional neural network has some similarities to the feed-forward neural network, where the
connections between units have weights that determine the influence of one unit on another unit. But a
CNN has one or more than one convolutional layers that use a convolution operation on the input and
then pass the result obtained in the form of output to the next layer. CNN has applications in speech and
image processing which is particularly useful in computer vision.
Modular Neural Network
A Modular Neural Network contains a collection of different neural networks that work independently
towards obtaining the output with no interaction between them. Each of the different neural networks
performs a different sub-task by obtaining unique inputs compared to other networks. The advantage of
this modular neural network is that it breaks down a large and complex computational process into
smaller components, thus decreasing its complexity while still obtaining the required output.
Radial basis function Neural Network
Radial basis functions are those functions that consider the distance of a point concerning the center. RBF
functions have two layers. In the first layer, the input is mapped into all the Radial basis functions in the
hidden layer and then the output layer computes the output in the next step. Radial basis function nets
are normally used to model the data that represents any underlying trend or function.
8. To understand the concept of the architecture of an artificial neural network,
we have to understand what a neural network consists of. In order to define
a neural network that consists of a large number of artificial neurons, which
are termed units arranged in a sequence of layers. Lets us look at various
types of layers available in an artificial neural network.
The architecture of an Artificial Neural Network
Artificial Neural Network primarily consists of three layers:
9. Input Layer
As the name suggests, it accepts inputs in several different formats provided by the programmer.
The hidden layer presents in-between input and output layers. It performs all the calculations to
find hidden features and patterns.
The input goes through a series of transformations using the hidden layer, which finally results in
output that is conveyed using this layer.
The artificial neural network takes input and computes the weighted sum of the inputs and includes a bias.
This computation is represented in the form of a transfer function.
It determines weighted total is passed as an input to an activation function to produce the output. Activation
functions choose whether a node should fire or not. Only those who are fired make it to the output layer. There
are distinctive activation functions available that can be applied upon the sort of task we are performing.
10. Artificial Neural Network can be best represented as a weighted directed graph, where the artificial
neurons form the nodes. The association between the neurons outputs and neuron inputs can be
viewed as the directed edges with weights. The Artificial Neural Network receives the input signal
from the external source in the form of a pattern and image in the form of a vector. These inputs
are then mathematically assigned by the notations x(n) for every n number of inputs.
Afterward, each of the input is multiplied by its
corresponding weights ( these weights are the details
utilized by the artificial neural networks to solve a
specific problem ). In general terms, these weights
normally represent the strength of the interconnection
between neurons inside the artificial neural network. All
the weighted inputs are summarized inside the
Working of Neural Network
11. If the weighted sum is equal to zero, then bias is added to make the output non-zero
or something else to scale up to the system's response. Bias has the same input, and
weight equals to 1. Here the total of weighted inputs can be in the range of 0 to
positive infinity. Here, to keep the response in the limits of the desired value, a certain
maximum value is benchmarked, and the total of weighted inputs is passed through
the activation function.
The activation function refers to the set of
transfer functions used to achieve the desired
output. There is a different kind of the activation
function, but primarily either linear or non-linear
sets of functions. Some of the commonly used
sets of activation functions are the Binary, linear,
and Tan hyperbolic sigmoidal activation
12. General Model of Neural Network
The following diagram represents the general model of ANN followed by its processing.
For this general model of artificial neural network,
the net input can be calculated as follows −
The output can be calculated by applying the
activation function over the net input.
We will pass the net input calculated inside the function for the final output
Data Processing including filtering & blind signal seperation
Game playing & decision making (chess , racing etc.)
Pattern Recognition (Face & Object Identification )
Sequence Recognition (Speech & Hand written text Recognition)
Data Validation & Risk Management
14. About: Finger prints are the unique pattern of ridges and valleys in every person’s fingers. Their patterns
are permanent and unchangeable for whole life of a person. They are unique and the probability that two
fingerprints are alike is only 1 in 1.9x10^15. Their uniqueness is used for identification of a person.
Image acquisition: the acquired image is digitalized into 512x512image with each pixel assigned a
particular gray scale value(raster image).
One of the Application in detail : Finger Print Recognition
Image Acquisition > Edge Detection > Ridge Extraction > Thining > Feature Extraction > Classification
Edge detection and Thinning: These are preprocessing of the image , remove noise and enhance
Feature extraction: This is the step where we pointout the features such as ridge
bifurcation and ridge endings of the finger print with the helpof neural network.
Classification: here a classlabel is assigned to theimage depending on theextracted
Store information on the entire network
The ability to work with insufficient knowledge
Good falt tolerance
Ability to train machine
The ability of parallel processing
The Advantages of Neural Networks:
Unexplained functioning of the network
Assurance of proper network structure
The difficulty of showing the problem to
The duration of the network is unknown
The Disadvantages of Neural Networks:
Neural networks are suitable for predicting time series mainly because of
learning only from examples, without any need to add additional
information that can bring more confusion than prediction effect. Neural
networks are able to generalize and are resistant to noise.
On the other hand, it is generally not possible to determine exactly what a
neural network learned and it is also hard to estimate possible prediction