1. INTRODUCTION TO ARTIFICIAL
NEURAL NETWORK

2. What is meant by neural
network?
 In information technology (IT), a neural network is
a system of hardware and/or software patterned
after the operation of neurons in the human brain.
Neural networks -- also called artificial neural
networks -- are a variety of deep learning
technology, which also falls under the umbrella of
artificial intelligence, or AI.

3. What is meant by Artificial neural
network?
 An artificial neuron network (ANN) is a
computational model based on the structure and
functions of biological neural networks. ... ANNs
are considered nonlinear statistical data modeling
tools where the complex relationships between
inputs and outputs are modeled or patterns are
found.

4. Introduction
 An Artificial Neuron Network (ANN), popularly
known as Neural Network is a computational
model based on the structure and functions of
biological neural networks. It is like an artificial
human nervous system for receiving, processing,
and transmitting information in terms of Computer
Science.

5. Basically, there are 3 different layers in a neural
network :-
 Input Layer (All the inputs are fed in the model
through this layer)
 Hidden Layers (There can be more than one hidden
layers which are used for processing the inputs
received from the input layers)
 Output Layer (The data after processing is made
available at the output layer)

6. Following is the manner in which these layers are laid

7. Input Layer
 The Input layer communicates with the external
environment that presents a pattern to the neural
network. Its job is to deal with all the inputs only.
This input gets transferred to the hidden layers
which are explained below. The input layer should
represent the condition for which we are training
the neural network. Every input neuron should
represent some independent variable that has an
influence over the output of the neural network

8. Hidden Layer
 The hidden layer is the collection of neurons
which has activation function applied on it and it
is an intermediate layer found between the input
layer and the output layer. Its job is to process the
inputs obtained by its previous layer. So it is the
layer which is responsible extracting the required
features from the input data. Many researches
has been made in evaluating the number of
neurons in the hidden layer but still none of them
was successful in finding the accurate result. Also
there can be multiple hidden layers in a Neural
Network.

9. Contd…
 So you must be thinking that how many hidden layers have to be
used for which kind of problem. Suppose that if we have a data
which can be separated linearly, then there is no need to use
hidden layer as the activation function can be implemented to
input layer which can solve the problem. But in case of problems
which deals with complex decisions, we can use 3 to 5 hidden
layers based on the degree of complexity of the problem or the
degree of accuracy required. That certainly not means that if we
keep on increasing the number of layers, the neural network will
give high accuracy! A stage comes when the accuracy becomes
constant or falls if we add an extra layer! Also, we should also
calculate the number of nuerons in each network. If the number
of neurons are less as compared to the complexity of the
problem data then there will be very few neurons in the hidden
layers to adequately detect the signals in a complicated data set.
If unnecessary more neurons are present in the network then
Overfitting may occur. Several methods are used till now which
do not provide the exact formula for calculating the number of
hidden layer as well as number of neurons in each hidden layer.

10. Output Layer
 The output layer of the neural network collects
and transmits the information accordingly in way it
has been designed to give. The pattern presented
by the output layer can be directly traced back to
the input layer. The number of neurons in output
layer should be directly related to the type of work
that the neural network was performing. To
determine the number of neurons in the output
layer, first consider the intended use of the neural
network.

11. Summation function =
X1Wi1+X2Wi2+…+XnWin

12. Biological Neural Network
 Our brain has a large network of interlinked
neurons, which act as a highway for information
to be transmitted from point A to point B. To send
different kinds of information from A to B, the
brain activates a different sets of neurons, and so
essentially uses a different route to get from A to
B. This is how a typical neuron might look like.

14.  At each neuron, its dendrites receive incoming
signals sent by other neurons. If the neuron receives
a high enough level of signals within a certain period
of time, the neuron sends an electrical pulse into the
terminals. These outgoing signals are then received
by other neurons.

15. Artificial Neural Network
 The ANN model is modelled after the biological
neural network (and hence its namesake).
Similarly, in the ANN model, we have an input
node (in this example we give it a handwritten
image of the number 6), and an output node,
which is the digit that the program recognized.

16. A simple Artificial Neural Network map, showing two
scenarios with two different inputs but with the same
output. Activated neurons along the path are shown in
red.

17. The main characteristics of an ANN is as such:
 Step 1. When the input node is given an image, it
activates a unique set of neurons in the first layer,
starting a chain reaction that would pave a unique
path to the output node. In Scenario 1, neurons A, B,
and D are activated in layer 1.
 Step 2. The activated neurons send signals to every
connected neuron in the next layer. This directly
affects which neurons are activated in the next layer.
In Scenario 1, neuron A sends a signal to E and G,
neuron B sends a signal to E, and neuron D sends a
signal to F and G.

18.  Step 3. In the next layer, each neuron is governed
by a rule on what combinations of received signals
would activate the neuron (rules are trained when we
give the ANN program training data, i.e. images of
handwritten digits and the correct answer). In
Scenario 1, neuron E is activated by the signals from
A and B. However, for neuron F and G, their
neurons’ rules tell them that they have not received
the right signals to be activated, and hence they
remains grey.
 Step 4. Steps 2-3 are repeated for all the remaining
layers (it is possible for the model to have more than
2 layers), until we are left with the output node.

19.  Step 5. The output node deduces the correct digit
based on signals received from neurons in the layer
directly preceding it (layer 2). Each combination of
activated neurons in layer 2 leads to one solution,
though each solution can be represented by different
combinations of activated neurons. In Scenarios 1 &
2, two images given to the input. Because the
images are different, the network activates a different
set of neurons to get from the input to the output.
However, the output is still able to recognise that
both images .

20. BNN versus ANN
Criteria BNN ANN
Processing
Massively parallel, slow
but superior than ANN
Massively parallel, fast
but inferior than BNN
Size
1011 neurons and 1015
interconnections
102 to 104 nodes (mainly
depends on the type of
application and network
designer)
Learning
They can tolerate
ambiguity
Very precise, structured
and formatted data is
required to tolerate
ambiguity
Fault tolerance
Performance degrades
with even partial damage
It is capable of robust
performance, hence has
the potential to be fault
tolerant
Storage capacity
Stores the information in
the synapse
Stores the information in
continuous memory
locations

21. Supervised learning
 Supervised learning as the name indicates a
presence of supervisor as teacher. Basically
supervised learning is a learning in which we teach
or train the machine using data which is well labelled
that means some data is already tagged with correct
answer. After that, machine is provided with new set
of examples(data) so that supervised learning
algorithm analyses the training data(set of training
examples) and produces an correct outcome from
labelled data.

22.  For instance, suppose you are given an basket
filled with different kinds of fruits. Now the first step is
to train the machine with all different fruits one by
one like this:

23.  If shape of object is rounded and depression at top
having color Red then it will be labelled as –Apple.
 If shape of object is long curving cylinder having color
Green-Yellow then it will be labelled as –Banana.
 Now suppose after training the data, you have given a
new separate fruit say Banana from basket and asked to
identify it.

24.  Since machine has already learnt the things from
previous data and this time have to use it wisely. It
will first classify the fruit with its shape and color, and
would confirm the fruit name as BANANA and put it
in Banana category. Thus machine learns the things
from training data(basket containing fruits) and then
apply the knowledge to test data(new fruit).
Supervised learning classified into two categories of
algorithms:
 Classification: A classification problem is when the
output variable is a category, such as “Red” or “blue”
or “disease” and “no disease”.
 Regression: A regression problem is when the
output variable is a real value, such as “dollars” or
“weight”.

25. Unsupervised learning
 Unsupervised learning is the training of machine
using information that is neither classified nor
labeled and allowing the algorithm to act on that
information without guidance. Here the task of
machine is to group unsorted information
according to similarities, patterns and differences
without any prior training of data.
 Unlike supervised learning, no teacher is
provided that means no training will be given to
the machine. Therefore machine is restricted to
find the hidden structure in unlabeled data by our-
self.

26.  For instance, suppose it is given an image having
both dogs and cats which have not seen ever.

27.  Thus machine has no any idea about the features of
dogs and cat so we can’t categorize it in dogs and
cats. But it can categorize them according to their
similarities, patterns and differences i.e., we can
easily categorize the above picture into two parts.
First first may contain all pics having dogs in it and
second part may contain all pics having cats in it.
Here you didn’t learn anything before, means no
training data or examples.

28.  Unsupervised learning classified into two categories
of algorithms:
 Clustering: A clustering problem is where you want
to discover the inherent groupings in the data, such
as grouping customers by purchasing behavior.
 Association: An association rule learning problem is
where you want to discover rules that describe large
portions of your data, such as people that buy X also
tend to buy Y.

29. Perceptron
 Perceptron is a single layer neural network
and a multi-layer perceptron is called Neural
Networks.Perceptron is a linear classifier (binary).
Also, it is used in supervised learning. It helps to
classify the given input data. But how the heck it
works ?
 A normal neural network looks like this as we all
know

31.  As you can see it has multiple layers.
 The perceptron consists of 4 parts .
 Input values or One input layer
 Weights and Bias
 Net sum
 Activation Function

32.  FYI: The Neural Networks work the same way as the
perceptron. So, if you want to know how neural
network works, learn how perceptron works.

33. But how does it work?
 The perceptron works on these simple steps
 a. All the inputs x are multiplied with their weights
w. Let’s call it k.

34.  b. Add all the multiplied values and call them
Weighted Sum.

35.  Apply that weighted sum to the correct Activation
Function.
 For Example : Unit Step Activation Function.

36. Why do we need Weights
and Bias?
 Weights shows the strength of the particular node.A
bias value allows you to shift the activation function
curve up or down.

37. Why do we need Activation Function?
 In short, the activation functions are used to map the
input between the required values like (0, 1) or (-1, 1).
 Where we use Perceptron?
 Perceptron is usually used to classify the data into two
parts. Therefore, it is also known as a Linear Binary
Classifier.