This slide is my work on my final bachelor project at the University of Kashan. I've worked on the improvement of Support Vector Machine using Deep Learning.

1. Deep Learning based on
Support Vector Machine
(SVM)
Peresenter : Parham Zilouchain Moghaddam
Supervisor : Dr. Javad Salimi

2. Deep Learning Introduction

4. Artificial Intelligence (AI)
Artificial Intelligence (AI) : Is a technique the enables a machine to mimic human behavior.

5. Machine Learning (ML)
Machine Learning (ML) : Is a technique to achieve AI through algorithms trained with data.

6. Deep Learning (DL)
Deep Learning (DL) :
Is a type of machine learning that is inspired by the structure of the human brain. In terms of deep learning this
structure is called an Artificial Neural Network

7. DL vs ML
7
• In the Machine Learning we have to tell the machine we have to tell
the machine the features based on witch the two can be
differentiated, features like size , type and etc.
Machine Learning
• In the Deep Learning the features are picked up by the neural network
without human intervention.
• and that kind of independence comes at cost of having a much higher
volume of data to train our machine.
Deep Learning

8. Data
Train them self’s to recognize the Patterns in this data.
Predict
A new set of similar Data

9. 12/20/2019 9
Neural Networks Prime Applications
Facial Recognition
Forecasting
FINANCIAL Investment
Music Composition

10. Neural Network ?

26. SVM ?

43. Our Problem ?

44. Cats vs Dogs
0 5000 10000 15000 20000 25000
Training
Validation
Data Description
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Dogs
Dogs
Total
Cats
Cats
Total

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Technologies Used
Python Anaconda
Pytorch Framework
Google Colab
Jupyter Notebook

46. Training Data
Extract Important Features
Validating
Results

47. Dive Into Code
Jupyter Notebook

48. Without SVM

49. With SVM

50. Cross Entropy Loss Without “SVM”

51. Cross Entropy Loss With “SVM”

52. Cross Entropy Loss Without “SVM”

53. Cross Entropy Loss With “SVM”

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Results
After Running the program
0
20
40
60
80
100
120
SVM - Train No SVM - Train SVM - Test No SVM - Test
Result Comparison
Accuracy
95% 73%
After Using SVM Before using SVM

55. With SVM Without SVM
Confusion Matrix’s

56. 87%
82 84 86 88 90 92 94 96
SVM
No SVM
Accuracy
Your logo
95%

57. 5
Your logo
Accuracy Accuracy
With SVM
95%
Without SVM
87%
Accuracy Comparison

58. 57
Your logo
0
10
20
30
40
50
60
70
80
90
100
SVM
78%SVM
65
70
75
80
85
90
No SVM
73%Without SVM
Accuracy

59. Without SVM With SVM
Conclusion

60. Further Steps

61. Any Questions ?

62. THANK YOU