Digital Image Processing Dr.S.Shajun Nisha

1. Dr.S.SHAJUN NISHA, MCA.,M.Phil.,M.Tech.,MBA.,Ph.D
Assistant Professor & Head
PG & Research Department of Computer Science
Sadakathullah Appa College
shajunnisha_s@yahoo.com
+91 99420 96220

2.  Artificial Intelligence is a technology that
make computers to act like human
 Artificial Intelligence is an umbrella term.
In which there are many subfields. They
are,
› Machine Learning
› Deep Learning
› Big Data
› Cloud Computing

3.  Machine Learning is a technique that
makes computer to take decisions or to
solve problems without being explicitly
programmed.
 Machine learns from experience that is
it learns from data.

4.  Naïve Bayes Classifier Algorithm
 K Means Clustering Algorithm
 Support Vector Machine Algorithm
 Apriori Algorithm
 Linear Regression
 Logistic Regression
 Artificial Neural Networks
 Random Forests
 Decision Trees
 Nearest Neighbours

5.  Learn something in –depth
 DL Uses Artificial Neural Network (ANN)
to make decision or to solve problem
 ANN-based on Biological Neural Network
(BNN)

7.  Dendrite: Receives signals from other
neurons
 Soma: Processes the information
 Axon: Transmits the output of this neuron
 Synapse: Point of connection to other
neurons

8.  Neuron: Basic computational unit of
ANN
 Input Layer: Receives input from the
dataset. Number of inputs refers the
number of features
 Hidden layer: The hidden layers greatly
contributes to the performance of the
model. A network can have a single
hidden layer or many hidden layers
which are connected together.
 Output Layer: Outcome of the model

9.  The type of hidden layer distinguishes
the different types of Neural Networks
 ANN
 CNN
 RNN
 The number of hidden layers is termed
as the depth of the neural network

10.  McCulloch Pitts Neuron
 Perceptron
 Sigmoid Neurons

11.  McCulloch-Pitts Neuron — Mankind’s
First Mathematical Model Of a Biological
Neuron
 McCulloch (neuroscientist) and Pitts
(logician) proposed a highly simplified
computational model of the neuron
(1943)
 Input and Output is binary

13.  g-aggregates the inputs and the function
f-takes a decision based on this aggregation
 The inputs can be excitatory or inhibitory
 y= 0 if any xi is inhibitory, else
 θ is called the thresholding parameter. This
is called Thresholding Logic

14.  Inhibitory input: if this input is 1 then
irrespective of other inputs, the output
is 0, that is the neuron is not going to
fire
 Excitatory input: is not something
which will cause the neuron to fire on
its own but it combine with other inputs
the neuron could be fire

15. Example: Whether I am going to watch a movie
“Bigil” or not.
Output: 1-Going to watch movie. 0-Never going to
watch movie
Here in the above example inhibitory input is
high hence the outputs is 0.

16.  OR- Output is High if any one of the
inputs is high
 AND- Output is High if all the inputs are
high
 XOR-Output is high if inputs are differ

17.  g(X)=g(x1, x2)=x1+x2
 OR function neuron would fire if ANY of
the inputs is ON i.e., g(X) ≥ 1 here.
 Where, Theta-ϴ=1

20.  g(X)=g(x1, x2)=x1+x2
 OR function neuron would fire if ANY of
the inputs is ON i.e., g(X) ≥ 2 here.
 Where, Theta-ϴ=2

23.  A single McCulloch Pitts Neuron can be
used to represent boolean functions which
are linearly separable.
 Linear separability (for boolean
functions) : There exists a line (plane)
such that all inputs which produce a 1
lie on one side of the line (plane) and
all inputs which produce a 0 lie on other
side of the line (plane)
 MP Neuron is not applicable for XOR.
Because, XOR is non linearly separable
function

25.  What about non-boolean (say, real)
inputs?
 Are all inputs equal? What if we want to
assign more importance to some inputs?
 What about functions which are not
linearly separable? Say XOR function.