Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka

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Use-Case Problem Statement
Using Artificial Neural Network, we need to figure out, if the bank notes are real or fake?

Use-Case Data Set
Data were extracted from images that were taken from genuine and forged banknote-like specimens. The final images
have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution
of about 660 dpi were gained. Wavelet Transform tool were used to extract features from images.
• Variance of Wavelet Transformed image
• Skewness of Wavelet Transformed image
• Curtosis of Wavelet Transformed image
• Entropy of image
Features
1 – Real, 0 - Fake
Label

Use-Case Implementation Steps
Start
Read the
Dataset
Define features
and labels
Divide the dataset into two
parts for training and testing
TensorFlow data structure for
holding features, labels etc..
Implement the model
Train the model
Reduce MSE (actual output –
desired output)
End
Repeat the process to
decrease the loss
Pre-processing of dataset
Make prediction on the test
data

Agenda
▪ Why Neural Networks?
▪ Motivation Behind Neural Networks
▪ What Are Neural Networks?
▪ Single Layer Perceptron
▪ Multi Layer Perceptron
▪ Implementation Of The Use-Case
▪ Applications Of Neural Networks

Why Neural Network?
We will begin by looking at the reason behind the introduction of Neural Networks

Problem Before Neural Networks
• Unless the specific steps that the computer needs to follow are known the computer cannot solve a
problem.
• This restricts the problem solving capability of conventional computers to problems that we already
understand and know how to solve.
The computer follows
a set of instructions in
order to solve a
problem

After Neural Networks
With Neural Networks computers can do things that we don't exactly know how to do.
Neural Networks learn
by example. They cannot
be programmed to
perform a specific task

Motivation Behind Neural
Networks
Let’s see the motivation behind Artificial Neural Network.

Motivation Behind Neural Networks
Neuron
Dendrite: Receives signals from
other neurons
Cell Body: Sums all the inputs
Axon: It is used to transmit
signals to the other cells
The building block of a neural net is the neuron. An artificial neuron works much the same
way the biological one does.

What Are Artificial Neural
Networks?
Now is the correct time to understand Artificial Neural Networks

What Are Artificial Neural Networks?
Artificial Neural Networks (ANNs) are computing systems inspired by the biological neural
networks that constitute animal brains. Such systems learn (progressively improve performance) to
do tasks by considering examples, generally without task-specific programming.
Training
Where is dog
in this pic?
Input
Without Neural Network

What Are Artificial Neural Networks?
Artificial Neural Networks (ANNs) are computing systems inspired by the biological neural
networks that constitute animal brains. Such systems learn (progressively improve performance) to
do tasks by considering examples, generally without task-specific programming.
Training
There is one
dog in this pic
Input
With Neural Network

How It Works?
Let’s focus on how Neural networks work?

How Artificial Neural Networks Work?
To get started, I'll explain artificial neuron called a perceptron.
X1
X2
Xn
Processing
Element
S = Xi Wi
Y
W1
W2
Wn
Y1
Y2
Yn
F(S)
Summation
Transfer
Function
Outputs
Artificial Neuron Biological Neuron

Modes In Perceptron
Using ModeTraining Mode
In the training mode, the
neuron can be trained to fire
(or not), for particular input
patterns
In the using mode, when a
taught input pattern is
detected at the input, its
associated output becomes
the current output

Activation Functions
Let’s look at various types of Activation Functions

Activation Function
Step Function Sigmoid Function
Sign Function

Perceptron Training With Analogy
Let’s understand this with an analogy

Perceptron Learning Algorithm – Beer Analogy
Suppose you want to go to a beer festival happening near your house. So your decision will depend
on multiple factors:
1. How is the weather?
2. Your wife is going with you?
3. Any public transport is available?

Inputs

Output
Output
‘O’
1
0

Let’s Prioritize Our Factors
X1 = 1 Output =1
Suppose for you the most important factor is weather, if it is not good you will definitely don’t go. Even if
the other two inputs are high. If it is good than definitely you will go.

Assign Weights
Now, let’s assign weights to our three inputs

Assign Weights
W1 = 6, W2 = 2, W3 = 2
Threshold = 5
W1 = 6, W2 = 2, W3 = 2
Threshold = 3
It will fire when weather is good and won’t fire if
weather is bad irrespective of the other inputs
It will fire when either x1 is high or the other two
inputs are high
W1 = Weight associated with input X1

Multilayer Perceptron – Artificial
Neural Network
Now, let’s look at multilayer perceptron

Multilayer Perceptron – Artificial Neural Network
As you know our brain is made up of millions of neurons, so a Neural Network is really just a composition of
perceptrons, connected in different ways and operating on different activation functions.
Input Layer
Hidden Layer 1
Hidden Layer 2
Output Layer

Example Of Artificial Neural
Networks
Let’s see an example where an Artificial Neural Network is used for image recognition

Artificial Neural Network - Example

Training A Neural Network
Let’s see how to train a Neural Network or a Multilayer Perceptron

The most common deep learning algorithm for supervised training of the multi-layer perceptrons is known as
backpropagation. In it, after the weighted sum of inputs and passing through the activation function we propagate
backwards and update the weights to reduce the error (desired output – model output). Consider the below example:
Input Desired Output
0 0
1 1
2 4

Input Desired Output Model Output
(W=3)
0 0 0
1 2 3
2 4 6
Let’s consider the initial value of the weight as 3 and see the model output

Input Desired
Output
Model Output
(W=3)
Absolute Error Square Error
0 0 0 0 0
1 2 3 1 1
2 4 6 2 4
Now, we will see the error (Absolute and Square)

Input Desired
Output
Model
Output
(W=3)
Absolute
Error
Square
Error
Model
Output
(W=4)
0 0 0 0 0 0
1 2 3 1 1 4
2 4 6 2 4 8
Let’s update the weight value and make it as 4

Input Desired
Output
Model
Output
(W=3)
Absolute
Error
Square
Error
Model
Output
(W=4)
Square
Error
0 0 0 0 0 0 0
1 2 3 1 1 4 4
2 4 6 2 4 8 16
Still there is error, but we can see that error has increased

W Error
W Error

Square
Error
Weight
Decrease
Weight
Increase
Weight

Applications Of Neural Networks
Modelling and Diagnosing the
Cardiovascular System
Electronic noses
Neural Network In Medicine Neural Network In Business
Marketing
Credit Evaluation

Session In A Minute
Why Neural Networks? What is Neural Network? What is Perceptron?
What is Multi Layer Perceptron? Training Of Multi Layer Perceptron Use-Case Implementation

Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka

Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka

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