Deep Learning - Exploring The Magical World of Neural Network

Minhas Kamal
Software Engineer,
SRBD, Dhaka, Bangladesh
Website: minhaskamal.github.io
Date: 29th Nov, 2017
Deep Learning
Exploring The Magical World of Neural Network
Version: 0.1

Contents
● Machine Learning
● Neuron
● Neural Network
● Solving A Problem with Neural
Network
TotalSlides-18

01
input
What Is Machine Learning?
output
data

01
input
output
data
......

01
input
output
......

02
We are Recruiting New Member
Input Output
Have
Graduated
Passed
Advanced Level
Selection
Decision

02
We are Recruiting New Member
Input Output
Have
Graduated
Passed
Advanced Level
Selection
Decision
1 1 1
1 0 0
0 1 0
0 0 0

03
We Use Neural Network
Have
Graduated
Passed
Advanced Level
Selection
Decision

04
Wait! What is a Neuron?
Dendrite AxonSoma

05
So … Neuron Is Like a Function
int neuron(int a, int b, …){
int result = ;
return result;
}

06
What Happens inside a Neuron?
a
b
c

06
a
b
c
×2
×0.5
×1

06
∑
a
b
c

06
> result
a
b
c

07
Neural Network Structure
Have
Graduated
Passed
Advanced Level
Selection
Decision

07
Graduated
(g)
Passed
(p)
Decision
(d)

07
Simplified Neural Network Structure
g
p
d

07
Simplified Neural Network Structure
∑ > 1 ?
g
p
d
×1
×1

08
Lets Count!!!
∑ > 1 ?
pg
d
×1×1
Input Output
g
(Have Graduated)
p
(Passed Advanced Level)
d
(Selection Decision)
1 1 1
1 0 0
0 1 0
0 0 0

09
Graduated
(g)
Passed
(p)
Decision
(d)

09
d
p
g

09
d
p
g
Input Layer Output Layer

09
d
p
g
weight 1
weight 2

10
int neuron(int g, int p){
int d = ;
return d;
}

10
int w1, w2;
int neuron(int g, int p){
int d = 0;
if((g*w1 + p*w2) > 1){
d = 1;
}
return d;
}

10
Actually … Neuron Is Like an Object
class Neuron{
int w1, w2;
int decide(int g, int p){
int d = 0;
if((g*w1 + p*w2) > 1){
d = 1;
}
return d;
}
}

10
The Neuron Object
class Neuron{
double w1, w2;
Neuron(double w1, double w2){
this.w1 = w1;
this.w2 = w2;
}
if((g*w1 + p*w2) > 1){
return 1;
}
return 0;
}
}

11
Wait!!!
Where We Get the
Weights?

12
The Neuron Object
class Neuron{
double w1, w2;
Neuron(double w1, double w2){...}
void learn(int g, int p, int d){}
if((g*w1 + p*w2) > 1){
return 1;
}
return 0;
}
}

12
The Neuron Object
class Neuron{
double w1, w2;
void learn(int g, int p, int d){
w1 = ;
w2 = ;
}
int decide(int g, int p){...}
}

09
d
p
g
w1
w2

12
The Neuron Object
class Neuron{
double w1, w2;
int error = decide(g, p) - d;
w1 = w1 - error * g;
w2 = w2 - error * p;
}
}

12
The Neuron Object
class Neuron{
double w1, w2;
w1 = w1 - 0.01 * error * g;
w2 = w2 - 0.01 * error * p;
}
}

12
class Neuron{
double w1, w2;
Neuron(double w1, double w2){
this.w1 = w1;
this.w2 = w2;
}
w1 = w1 - 0.01 * error * g;
w2 = w2 - 0.01 * error * p;
}
if((g*w1 + p*w2) > 1){
return 1;
}
return 0;
}
}
TheFullCode

13
Lets Run IT!
public statis void main(){
Neuron neuron = new Neuron(0.2, 3.1);
//learning
neuron.learn(1, 1, 1);
//testing
System.out.println(neuron.decide(1, 1));
}

14
Lets Run IT Again!
//learning
for(int i=0; i<500; i++){
}
//testing
}

15
Lets Run IT for a New Problem!
//learning
for(int i=0; i<500; i++){
}
//testing
}

16
What about This Problem?
//learning
for(int i=0; i<500; i++){
}
//testing
}

16
We Did Not Consider Threshold
∑ > T ?
g
p
d
×w1
×w2

16
class Neuron{
double w1, w2, th;
Neuron(double w1, double w2, double th){
this.w1 = w1;
this.w2 = w2;
this.th = th;
}
w1 = w1 - 0.01 * error * g;
w2 = w2 - 0.01 * error * p;
th = th + 0.01 * error;
}
if((g*w1 + p*w2) > th){
return 1;
}
return 0;
}
}
TheFullCode

16
Run Again
Neuron neuron = new Neuron(0.2, 3.1, 1.7);
//learning
for(int i=0; i<500; i++){
}
//testing
}

OK… But Where Is Deep Learning?

17
Lets Use Different Types of Neurons

17
Even I Donno What That Thing Is…

18
OK… This Is the Last One
Neuron neuron = new Neuron(0.2, 3.1, 1.7);
//learning
for(int i=0; i<500; i++){
}
//testing
}

There are much to learn.
Next - we dive even deeper!

Deep Learning - Exploring The Magical World of Neural Network

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