اسلاید روز ششم از کارگاه ۷ روزه داده‌های بزرگ و یادگیری ماشین که با تاکید بر یادگیری ژرف برگزار شد. جلسه ششم کارگاه نیز به یادگیری ژرف و کاربردها اختصاص خواهد یافت. این کارگاه به همت ای‌سی‌ام دانشگاه تهران در محل دانشکده فنی برگزار می‌شود زمان هر جلسه ۲ ساعت است

1. 1
۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬
‫ﺻﺪﯾﻘﯽ‬ ‫ﺍاﻣﯿﺮ‬
‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
@amirsedighi :‫ﺗﻮ4ﺘﺮ‬
:‫ﺍاﯾﻤﯿﻞ‬sedighi@gmail.com
‫ﺩدﻭوﻡم‬ ‫ﻗﺴﺖ‬ - ‫ﮊژﺭرﻑف‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬ - ‫ﺷﺸﻢ‬ ‫ﺭرﻭوﺯز‬

2. ‫ﺻﺪﯾﻘﯽ‬ ‫ﺍاﻣﯿﺮ‬
:‫ﻣﻮﺳﺲ‬
2
‫ﻣﻌﺮﻓﯽ‬
http://recommender.ir
http://helio.ir
http://commentum.ir
@amirsedighi :‫ﺗﻮ4ﺘﺮ‬
:‫ﺍاﯾﻤﯿﻞ‬sedighi@gmail.com

3. 3
‫‌ﻫﺎ‬‫ﺮ‬‫ﮐﺎﻣﭙﯿﻮﺗ‬ ‫ﺗﮑﺎﻣﻞ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬

4. 4
‫ﺍاﻧﺴﺎﻥن‬ ‫ﺩدﺍاﻧﺶ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬

5. 5
‫ﺍاﻧﺴﺎﻥن‬ ‫ﺩدﺍاﻧﺶ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬

6. 6
‫ﺍاﻧﺴﺎﻥن‬ ‫ﺩدﺍاﻧﺶ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬
‫ﺫذﻫﻨﯽ‬ ‫ﻭو‬ ‫ﺑﺼﺮﯼی‬
subjective and intuitive
‫‌ﻧﺪﺍاﺭرﺩد‬ ‫ﻓﺮﻣﻮﻝل‬

7. 7
‫ﺍاﻧﺴﺎﻥن‬ ‫ﺩدﺍاﻧﺶ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬
‫ﺫذﻫﻨﯽ‬ ‫ﻭو‬ ‫ﺑﺼﺮﯼی‬
subjective and intuitive
‫‌ﻧﺪﺍاﺭرﺩد‬ ‫ﻓﺮﻣﻮﻝل‬
‫ﻣﺎ‬ ‫ﻣﺜﻞ‬

8. 8
‫ﺍاﻧﺴﺎﻥن‬ ‫ﺩدﺍاﻧﺶ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

9. 9
‫ﺍاﻧﺴﺎﻥن‬ ‫ﺩدﺍاﻧﺶ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
،‫ﻧﯿﺴﺖ‬ ‫ﭘﺬﯾﺮ‬ ‫ﻓﺮﻣﻮﻝل‬ ‫ﭼﻨﺪﺍاﻥن‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬ ‫ﮐﻪ‬ ‫ﻣﻮﺍاﺭرﺩدﯼی‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎ‬‫ﯽ‬‫ﻭوﯾﮋﮔ‬ ‫ﺷﻨﺎﺳﺎﯾﯽ‬
.‫ﺍاﺳﺖ‬ ‫ﺩدﺷﻮﺍاﺭر‬ ‫ﺑﺴﯿﺎﺭر‬

10. 10
‫ﺍاﻧﺴﺎﻥن‬ ‫ﺩدﺍاﻧﺶ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
…‫ﺩدﺍاﺭرﺩد‬ ‫ﺷﯿﺸﻪ‬ ،‫ﺍاﺳﺖ‬ ‫ﻓﻠﺰﯼی‬ ،‫ﺩدﺍاﺭرﺩد‬ ‫ﭘﻼﮎک‬ ،‫ﺩدﺍاﺭرﺩد‬ ‫ﭼﺮﺥخ‬ :‫ﻣﺎﺷﯿﻦ‬
… ‫ﻣﯿﺪﺍاﺭرﺩد‬ ‫ﺑﺮ‬ ‫ﻗﺪﻡم‬ ،‫ﺩدﺍاﺭرﺩد‬ ‫ﺗﻨﻪ‬ ،‫ﺩدﺍاﺭرﺩد‬ ‫ﺳﺮ‬ ،‫ﺩدﺍاﺭرﺩد‬ ‫ﭘﺎ‬ ‫ﻭو‬ ‫ﺩدﺳﺖ‬ :‫ﺍاﻧﺴﺎﻥن‬

11. 11
‫ﺍاﻧﺴﺎﻥن‬ ‫ﺩدﺍاﻧﺶ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺳﺖ‬ ‫ﺳﺎﺩدﻩه‬ ‫ﭼﺮﺥخ‬ ‫ﻫﻨﺪﺳﯽ‬ ‫ﺗﺼﻮﯾﺮ‬

12. 12
‫ﺍاﻧﺴﺎﻥن‬ ‫ﺩدﺍاﻧﺶ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
…‫ﻭوﺍاﻗﻌﯽ‬ ‫ﺩدﻧﯿﺎﯼی‬ ‫ﺩدﺭر‬ ‫ﻭوﻟﯽ‬
،‫ﻣﻮﺍاﺩد‬ ‫ﺟﻨﺲ‬ ،‫‌ﻫﺎ‬‫ﻪ‬‫ﺳﺎﯾ‬ ،‫ﻣﺎ‬ ‫ﻧﮕﺎﻩه‬ ‫ﺯزﺍاﻭوﯾﻪ‬ ،‫ﻧﻮﺭر‬ ‫ﺯزﺍاﻭوﯾﻪ‬
…‫ﺧﻮﺭرﺷﯿﺪ‬ ‫ﻧﻮﺭر‬ ‫ﺑﺎﺯزﺗﺎﺏب‬ ،‫ﻧﮕﺎﺭر‬ ‫ﻭو‬ ‫ﻧﻘﺶ‬ ،‫‌ﻫﺎ‬‫ﺮ‬‫ﮔﻠﮕﯿ‬

13. 13
‫ﺍاﻧﺴﺎﻥن‬ ‫ﺩدﺍاﻧﺶ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﻣﻘﺎﺩدﯾﺮ‬ ‫ﻗﺎﻟﺐ‬ ‫ﺩدﺭر‬ ‫ﭼﺮﺥخ‬ ‫ﺗﻮﺻﯿﻒ‬
‫ﻧﯿﺴﺖ‬ ‫ﺳﺎﺩدﻩه‬ ،‫ﺗﺼﻮﯾﺮ‬ ‫‌ﻫﺎﯼی‬‫ﻞ‬‫ﭘﯿﮑﺴ‬

14. 14
‫ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
The solution is to use machine learning to discover not only the
mapping from representation to output, but also the representation
itself.

15. 15
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
The solution is to use machine learning to discover not only the
mapping from representation to output, but also the representation
itself.
Representation Learning
‫ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬

16. 16
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
The solution is to use machine learning to discover not only the
mapping from representation to output, but also the representation
itself.
Representation Learning Higher Performance than hand-designed
‫ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
Accelerate adaption with
minimal human interaction

17. 17
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Factors of Variation
When designing features or algorithms for learning features, our goal is
usually to separate the factors of variation that explain the observed data.

18. 18
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Factors of Variation
Factors are separated sources of influence:
They are not directly observable.
When designing features or algorithms for learning features, our goal is
usually to separate the factors of variation that explain the observed data.

19. 19
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Factors of Variation
Factors are separated sources of influence:
Such factors are often not quantities.
They are not directly observable.
When designing features or algorithms for learning features, our goal is
usually to separate the factors of variation that explain the observed data.

20. 20
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Factors of Variation
They may exist either as unobserved objects or unobserved forces in the
physical world that affect observable quantities.
Factors are separated sources of influence:
Such factors are often not quantities.
They are not directly observable.
When designing features or algorithms for learning features, our goal is
usually to separate the factors of variation that explain the observed data.

21. 21
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Factors of Variation
They may exist either as unobserved objects or unobserved forces in the
physical world that affect observable quantities.
They may also exist as constructs in the human mind that provide useful
simplifying explanations or inferred causes of the observed data.
Factors are separated sources of influence:
Such factors are often not quantities.
They are not directly observable.
When designing features or algorithms for learning features, our goal is
usually to separate the factors of variation that explain the observed data.

22. 22
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Factors of Variation
Factors of Variation:
Position

23. 23
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Factors of Variation
Factors of Variation:
Position
Color/texture

24. 24
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Factors of Variation
Factors of Variation:
Position
Color/texture
The angle/brightness of the sun

25. 25
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Factors of Variation
Factors of Variation:
Position
Color/texture
The angle/brightness of the sun
The weather (Snow/Rain/Fog/Dust)

26. 26
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Factors of Variation
Factors of Variation:
Position
Color/texture
The angle/brightness of the sun
The weather (Snow/Rain/Fog/Dust)
The amount of load

27. 27
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Factors of Variation
Factors of Variation:
Position
Color/texture
The angle/brightness of the sun
The weather (Snow/Rain/Fog/Dust)
The amount of load
Exterior Accessories

28. 28
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Factors of Variation
Factors of Variation:
Position
Color/texture
The angle/brightness of the sun
The weather (Snow/Rain/Fog/Dust)
The amount of load
Exterior Accessories
The individual pixels in an
image of a red car might be
very close to black at night…

29. 29
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Factors of Variation:
Position
Color/texture
The angle/brightness of the sun
The weather (Snow/Rain/Fog/Dust)
The amount of load
Exterior Accessories
The individual pixels in an
image of a red car might be
very close to black at night…
‫ﮐﺮﺩد؟‬ ‫ﺑﺎﯾﺪ‬ ‫ﭼﻪ‬

30. 30
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
.‫ﺑﮑﺎﻫﯿﻢ‬ (‫ﻭوﺍاﻗﻌﯽ‬ ‫)ﺩدﻧﯿﺎﯼی‬ ‫ﻭوﺭرﻭوﺩدﯼی‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫ﭘﯿﭽﯿﺪﮔﯽ‬ ‫ﺍاﺯز‬ ‫ﺑﺎﯾﺪ‬ ‫‌ﻫﺎ‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺍاﻏﻠﺐ‬ ‫ﺩدﺭر‬
‫ﺑﺮﺍاﯼی‬ ‫ﻋﻤﻮﻣﯽ‬ ‫‌ﻫﺎﯼی‬‫ﮏ‬‫ﺗﮑﻨﯿ‬ ‫ﺍاﺯز‬ ‫ﯾﮑﯽ‬ .‫ﺩدﺍاﺭرﺩد‬ ‫ﻭوﺟﻮﺩد‬ ‫ﻣﻨﻈﻮﺭر‬ ‫ﺍاﯾﻦ‬ ‫ﺑﺮﺍاﯼی‬ ‫ﻣﺘﻨﻮﻋﯽ‬ ‫‌ﻫﺎﯼی‬‫ﺵش‬‫ﺭرﻭو‬
.‫ﺍاﺳﺖ‬ ”‫ﺍاﺑﻌﺎﺩد‬ ‫“ﮐﺎﻫﺶ‬ ،‫ﭘﯿﭽﯿﺪﮔﯽ‬ ‫ﺍاﺯز‬ ‫ﮐﺎﺳﺘﻦ‬
.‫‌ﺩدﻫﯿﻢ‬‫ﯽ‬‫ﻣ‬ ‫ﺍاﻓﺰﺍاﯾﺶ‬ ‫ﺭرﺍا‬ ‫ﺍاﻧﺘﺰﺍاﻉع‬ ‫ﺳﻄﺢ‬ ،‫ﺍاﺑﻌﺎﺩد‬ ‫ﮐﺎﻫﺶ‬ ‫ﮐﻤﮏ‬ ‫ﺑﻪ‬
‫ﭘﯿﭽﯿﺪﻩه‬ ‫‌ﺍاﯼی‬‫ﻩه‬‫ﭘﺪﯾﺪ‬ ‫ﺍاﺯز‬ ‫ﺷﺪﻩه‬ ‫ﺳﺎﺩدﻩه‬ ‫ﺑﺎﺯزﻧﻤﺎﯾﯽ‬
‫#ﺷﻮﺧﯽ‬

31. 31
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
.‫ﮐﻨﯿﻢ‬ ‫ﺗﻮﻟﯿﺪ‬ ‫ﮐﻮﭼﮏ‬ ‫ﻭو‬ ‫ﻓﺸﺮﺩدﻩه‬ ‫ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ ‫ﯾﮏ‬ ‫ﻭوﺍاﻗﻌﯽ‬ ‫ﺩدﻧﯿﺎﯼی‬ ‫ﻭوﺭرﻭوﺩدﯼی‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫ﺍاﺯز‬ ‫ﺑﺎﯾﺪ‬
‫ﺑﻪ‬ ‫ﺷﺒﯿﻪ‬ ‫‌ﻫﺎﯾﯽ‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫ﺑﻪ‬ ‫ﻓﺸﺮﺩدﻩه‬ ‫ﺑﺎﺯزﻣﻨﺎﯾﯽ‬ ‫ﺍاﺯز‬ ،‫ﻋﮑﺲ‬ ‫ﻣﺴﯿﺮ‬ ‫ﻃﯽ‬ ‫ﺑﺎ‬ ‫‌ﺗﻮﺍاﻧﯿﻢ‬‫ﯽ‬‫ﻣ‬ ‫ﺳﭙﺲ‬
.‫ﺑﺮﺳﯿﻢ‬ (‫ﺗﺮ‬ ‫ﺳﺎﺩدﻩه‬ ‫)ﻭوﻟﯽ‬ ‫ﻭوﺭرﻭوﺩدﯼی‬ ‫ﺩدﺍاﺩدﻩه‬
‫ﭘﯿﭽﯿﺪﻩه‬ ‫‌ﺍاﯼی‬‫ﻩه‬‫ﭘﺪﯾﺪ‬ ‫ﺍاﺯز‬ ‫ﺷﺪﻩه‬ ‫ﺳﺎﺩدﻩه‬ ‫ﺑﺎﺯزﻧﻤﺎﯾﯽ‬
‫#ﺷﻮﺧﯽ‬

32. 32
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬
Deep learning introduces representations that are expressed in
terms of other, simpler representations.

33. 33
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬

34. 34
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬
Car
Person
Animal

35. 35
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬
Raw Sensory Input Data
Car
Person
Animal

36. 36
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬
Raw Sensory Input Data The function mapping from a set of pixels
to an object identity is very complicated.
Car
Person
Animal
!‫ﻧﯿﺴﺖ‬ ‫ﻣﻤﮑﻦ‬ ‫ﻣﺴﺘﻘﯿﻢ‬ ‫ﺗﺒﺪﯾﻞ‬

37. 37
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬

38. 38
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Edges
Contours
/Corners
Object
Parts
‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬

39. 39
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫‌ﻫﺎﯼی‬ ‫ﻻﯾﻪ‬
‫ﻣﺨﻔﯽ‬
‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬

40. 40
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫‌ﻫﺎﯼی‬‫ﺖ‬‫ﻧﮕﺎﺷ‬
‫ﺑﻪ‬ ‫ﮐﻪ‬ ‫ﺳﺎﺩدﻩه‬
‫ﺻﻮﺭرﺕت‬
‫ﺍاﻓﺰﺍاﯾﺸﯽ‬
‫‌ﻫﺎﯼی‬‫ﺮ‬‫ﻓﯿﭽ‬
‫ﺭرﺍا‬ ‫ﺗﺼﻮﯾﺮ‬
‫ﺷﻨﺎﺳﺎﯾﯽ‬
‫‌ﮐﻨﻨﺪ‬‫ﯽ‬‫ﻣ‬
‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬

41. 41
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺗﻨﻬﺎ‬ ‫ﺍاﻭوﻝل‬ ‫ﻻﯾﻪ‬
‫ﺷﻨﺎﺳﺎﯾﯽ‬ ‫ﺑﻪ‬
‫ﺍاﺷﯿﺎ‬ ‫‌ﻫﺎﯼی‬‫ﻪ‬‫ﻟﺒ‬
.‫‌ﭘﺮﺩدﺍاﺯزﺩد‬‫ﯽ‬‫ﻣ‬
‫ﺑﺎ‬ ‫ﮐﺎﺭر‬ ‫ﺍاﯾﻦ‬
‫ﻣﻘﺎﯾﺴﻪ‬
‫ﺭرﻭوﺷﻨﺎﯾﯽ‬
‫‌ﻫﺎﯼی‬‫ﻞ‬‫ﭘﯿﮑﺴ‬
‫ﺍاﻧﺠﺎﻡم‬ ‫ﻣﺠﺎﻭوﺭر‬
‫‌ﺷﻮﺩد‬‫ﯽ‬‫ﻣ‬
‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬

42. 42
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﻻﯾﻪ‬ ‫ﺧﺮﻭوﺟﯽ‬
‫ﻻﯾﻪ‬ ‫ﺑﻪ‬ ‫ﺍاﻭوﻝل‬
‫ﻣﯿﺮﺳﺪ‬ ‫ﺩدﻭوﻡم‬
‫ﺩدﻭوﻡم‬ ‫ﻻﯾﻪ‬ ‫ﻭو‬
‫ﺑﺴﺎﺩدﮔﯽ‬
‫ﻭو‬ ‫ﻫﺎ‬ ‫ﮔﻮﺷﻪ‬
‫‌ﻫﺎ‬‫ﻞ‬‫ﻓﺎﺻ‬ ‫ﺣﺪ‬
‫ﭘﯿﺪﺍا‬ ‫ﺭرﺍا‬
‫ﺁآﻧﻬﺎ‬ .‫‌ﮐﻨﺪ‬‫ﯽ‬‫ﻣ‬
‌‫ﻪ‬‫ﻣﺠﻤﻮﻋ‬
‫ﻟﺒﻪ‬ ‫ﺍاﺯز‬ ‫ﻫﺎﯾﯽ‬
.‫ﻫﺴﺘﻨﺪ‬ ‫ﻫﺎ‬
‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬

43. 43
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﻪ‬ ‫ﺳﻮﻡم‬ ‫ﻻﯾﻪ‬
‫ﮐﻤﮏ‬
‫ﻭو‬ ‫‌ﻫﺎ‬‫ﻪ‬‫ﮔﻮﺷ‬
‫‌ﻫﺎﯾﯽ‬‫ﻪ‬‫ﻓﺎﺻﻠ‬
‫ﻻﯾﻪ‬ ‫ﺍاﺯز‬ ‫ﮐﻪ‬
‫ﺩدﺭر‬ ‫ﺩدﻭوﻡم‬
،‫ﮐﺮﺩدﻩه‬ ‫ﯾﺎﻓﺖ‬
‫ﺭرﺍا‬ ‫ﺍاﺷﯿﺎ‬
‫ﺷﻨﺎﺳﺎﯾﯽ‬
.‫‌ﮐﻨﺪ‬‫ﯽ‬‫ﻣ‬
‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬

44. 44
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬
‫ﺍاﺳﺖ‬ ‫ﺗﺎﺑﻊ‬ ‫ﯾﮏ‬
x
y

45. 45
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Autoencoder
.‫ﺍاﺳﺖ‬ (Autoencoder) ‫ﺧﻮﺩدﺭرﻣﺰﻧﮕﺎﺭر‬ ،(Presentation Learning) ‫ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ ‌‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﺍاﻟﮕﻮﺭرﯾﺘﻢ‬ ‫ﯾﮏ‬ ‫ﺟﻮﻫﺮ‬

46. 46
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Autoencoder - ‫ﺧﻮﺩدﺭرﻣﺰﻧﮕﺎﺭر‬
.‫ﺍاﺳﺖ‬ (Autoencoder) ‫ﺧﻮﺩدﺭرﻣﺰﻧﮕﺎﺭر‬ ،(Presentation Learning) ‫ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ ‌‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﺍاﻟﮕﻮﺭرﯾﺘﻢ‬ ‫ﯾﮏ‬ ‫ﺟﻮﻫﺮ‬
‫ﺍاﺳﺖ‬ ‫ﺗﻮﺟﻬﯽ‬ ‫ﻗﺎﺑﻞ‬ ‫‌ﻫﺎﯼی‬‫ﯽ‬‫ﻭوﯾﮋﮔ‬ ‫ﺩدﺍاﺭرﺍاﯼی‬ ،‫ﺟﺪﯾﺪ‬ ‫ﺑﺎﺯزﻧﻤﺎﯾﯽ‬

47. 47
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
.‫ﺍاﺳﺖ‬ Autoencoder ،‫ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ ‌‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﺍاﻟﮕﻮﺭرﯾﺘﻢ‬ ‫ﯾﮏ‬ ‫ﺟﻮﻫﺮ‬
Autoencoder
Input = decoder(encoder(input))

48. 48
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
.‫ﺍاﺳﺖ‬ Autoencoder ،‫ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ ‌‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﺍاﻟﮕﻮﺭرﯾﺘﻢ‬ ‫ﯾﮏ‬ ‫ﺟﻮﻫﺮ‬
Autoencoder
Input = decoder(encoder(input))
An autoencoder is the combination of an encoder function that converts
the input data into a different representation, and a decoder function that
converts the new representation back into the original format.

49. 49
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
.‫ﺍاﺳﺖ‬ Autoencoder ،‫ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ ‌‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﺍاﻟﮕﻮﺭرﯾﺘﻢ‬ ‫ﯾﮏ‬ ‫ﺟﻮﻫﺮ‬
Autoencoder
Input = decoder(encoder(input))
Reconstructed Original
An autoencoder is the combination of an encoder function that converts
the input data into a different representation, and a decoder function that
converts the new representation back into the original format.

50. 50
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Autoencoder

51. 51
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺟﺎﻭوﺍا‬ ‫ﺩدﺭر‬ ‫ﮐﺪ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬

52. 52
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺟﺎﻭوﺍا‬ ‫ﺩدﺭر‬ ‫ﮐﺪ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬

53. 53
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺟﺎﻭوﺍا‬ ‫ﺩدﺭر‬ ‫ﮐﺪ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬
Encoder
Decoder

54. 54
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
‫ﺍاﻟﺰﺍاﻣﯽ‬ ،‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬ ‫‌ﻫﺎﯼی‬‫ﮏ‬‫ﺗﮑﻨﯿ‬ ‫ﺯزﺩدﻥن‬ ‫ﻣﺤﮏ‬ ‫ﺑﺮﺍاﯼی‬
.‫ﻧﯿﺴﺖ‬ ‫ﻧﻮﯾﺴﯽ‬ ‫ﺑﺮﻧﺎﻣﻪ‬ ‫ﻓﻨﻮﻥن‬ ‫ﺑﺮ‬ ‫ﺗﺴﻠﻂ‬ ‫ﻭو‬ ‫ﺁآﮔﺎﻫﯽ‬ ‫ﺑﻪ‬
‫ﺑﻪ‬ ‫ﻧﯿﺎﺯز‬ ‫ﺑﺪﻭوﻥن‬ ‫ﮐﻪ‬ ‫‌ﺷﻮﯾﻢ‬‫ﯽ‬‫ﻣ‬ ‫ﺁآﺷﻨﺎ‬ ‫ﺍاﺑﺰﺍاﺭرﯼی‬ ‫ﺑﺎ‬ ‫ﺍاﯾﻨﺠﺎ‬ ‫ﺩدﺭر‬
‫ﯾﺎﺩدﮔﯿﺮﯼی‬ ‫‌ﻫﺎﯼی‬‫ﮏ‬‫ﺗﮑﻨﯿ‬ ‫‌ﻫﺎﯼی‬‫ﯽ‬‫ﺗﻮﺍاﻧﺎﯾ‬ ‫‌ﻧﻮﯾﺴﯽ‬‫ﻪ‬‫ﺑﺮﻧﺎﻣ‬
‫‌ﺍاﻧﺪﺍاﺯزﯼی‬‫ﻩه‬‫ﺭرﺍا‬ ،‫ﻧﺼﺐ‬ .‫‌ﺩدﻫﺪ‬‫ﯽ‬‫ﻣ‬ ‫ﻗﺮﺍاﺭر‬ ‫ﻣﺎ‬ ‫ﺍاﺧﺘﯿﺎﺭر‬ ‫ﺩدﺭر‬ ‫ﺭرﺍا‬ ‫‌ﻣﺎﺷﯿﻦ‬
.‫ﺍاﺳﺖ‬ ‫ﺳﺎﺩدﻩه‬ ‫ﺑﺴﯿﺎﺭر‬ ‫ﺍاﺑﺰﺍاﺭر‬ ‫ﺍاﯾﻦ‬ ‫ﺑﮑﺎﺭرﮔﯿﺮﯼی‬ ‫ﻭو‬

55. 55
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
http://www.skytree.net/

56. 56
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
What is Skytree Express?
• Skytree Express is an express way to use Skytree’s machine learning
software. It is available for use via a Virtual Machine (VM) for Windows,
MAC OS X, and through an easy install script on RHEL/CentOS systems.
• Currently, Skytree Express comes in two versions (i) Skytree GUI & Python
SDK and (ii) Skytree Command Line Interface (CLI).
• Skytree Express can be downloaded free of cost. It comes with a
preconfigured license that is valid for one year.

57. 57
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬

58. 58
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬

59. 59
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Standard Data Sources:
• Relational Databases (RDBMS)
• Hadoop Systems (HDFS)
• Flat File Databases (e.g. CSV)
Machine Learning Methods:
• Random Decision Forests
• Kernel Density Estimation
• K-means
• Singular Value Decomposition
• Gradient Boosting
• Decision Tree
• 2-point Correlation
• Range Searching
• K-nearest Neighbours Algorithm
• Linear Regression
• Support Vector Machine
• Logistic Regression
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬

60. 60
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬

61. 61
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬

62. 62
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬

63. 63
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
http://pages.skytree.net/free-download-GUI.html?
utm_medium=website&utm_source=skytree
http://pages.skytree.net/free-downloadCLI-web.html
GUI
CLI
Download:

64. 64
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
Skytree Express is available for personal, educational, and even
commercial usage! The free version restricts usage up to 100 million
elements on a single machine/node.
License:

65. 65
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬

66. 66
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
1. Download Skytree Express edition.
2. Unzip
3. Run it Using Virtual Box
4. Login: ssh -p 2222 skytree@localohst
password: skytree

67. 67
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
> skytree-server --help
===========================================================================
SKYTREE (R) - THE MACHINE LEARNING COMPANY (R) - http://www.skytree.net/
(C) 2010-2016 Skytree Inc. All rights reserved.
===========================================================================
Release: Skytree Infinity 15.5.0
---------------------------------------------------------------------------
Local time: 2016-Sep-06 08:25:10
Username: skytree
Hostname: localhost.localdomain
System: CentOS release 6.5 (Final)
Processor: Intel(R) Core(TM) i7 CPU M 620 @ 2.67GHz
# CPU Cores: 2
Total Memory: 3.74 GB
Free Memory: 3.61 GB
System Load: 0.00 %
---------------------------------------------------------------------------
Working directory: /home/skytree
Command-line arguments:
skytree-server --help
---------------------------------------------------------------------------

68. 68
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
08:25:10 [INFO] usage: skytree-server <module> <arguments>
08:25:10 [INFO] Available modules:
08:25:10 [INFO] Discovery Features Enabled
08:25:10 [INFO] nnplus
08:25:10 [INFO] nn
08:25:10 [INFO] whatif
08:25:10 [INFO] svd
08:25:10 [INFO] two_pt
08:25:10 [INFO] kmeans
08:25:10 [INFO] Distributed Features Enabled
08:25:10 [INFO] status
08:25:10 [INFO] Prediction Features Enabled
08:25:10 [INFO] rdf
08:25:10 [INFO] rdfr
08:25:10 [INFO] gbt
08:25:10 [INFO] gbtr
08:25:10 [INFO] automodel
08:25:10 [INFO] glmc
08:25:10 [INFO] glmr
08:25:10 [INFO] logistic
08:25:10 [INFO] nnc
08:25:10 [INFO] wnnc
08:25:10 [INFO] score
08:25:10 [INFO] score-recommendation
08:25:10 [INFO] svm
08:25:10 [INFO] svm14
08:25:10 [INFO] Recommendation Features Enabled
08:25:10 [INFO] cf
08:25:10 [INFO] General options:
08:25:10 [INFO] --help Print this information.
08:25:10 [INFO] --watchdog [=arg(=on)] (=1) If set, monitor system resources
and
08:25:10 [INFO] warn if they are running low.
08:25:10 [INFO] --watchdog_low_memory_threshold arg (=0.05)
08:25:10 [INFO] The watchdog warns if the amount of
08:25:10 [INFO] available system memory is less than
08:25:10 [INFO] the specified fraction.
08:25:10 [INFO] --watchdog_high_load_threshold arg (=1.5)
08:25:10 [INFO] The watchdog warns if the (normalized)
08:25:10 [INFO] system load is higher than the
08:25:10 [INFO] specified value.
08:25:10 [INFO] --log arg If given, write log to this file
08:25:10 [INFO] instead of stdout.
08:25:10 [INFO] --loglevel arg (=default) Level of log detail. One of:
08:25:10 [INFO] verbose: log everything
08:25:10 [INFO] default: log messages and warnings
08:25:10 [INFO] warning: log only warnings
08:25:10 [INFO] silent : no logging
08:25:10 [INFO] --input_file arg If given, load input options from this
08:25:10 [INFO] file.
08:25:10 [INFO] --hosts arg Comma-separated list of hosts to run
08:25:10 [INFO] the distributed version of Skytree
08:25:10 [INFO] Infinity on.
08:25:10 [INFO] --procs_per_host arg Number of processes per host.
08:25:10 [INFO] --fast_read [=arg(=on)] (=0) This option is deprecated.

69. 69
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
$ skytree-server gbt --help
$ ./sample_script.sh

70. 70
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬

71. 71
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
$ skytree-server gbt --training_in income.data.st --training_labels_in income.data.labels
--num_trees 100 --model_out gbt.simple.model | tee gbt.simple.train.log
$ less gbt.simple.lables
$ skytree-server gbt --testing_in income.test.st --model_in gbt.simple.model
--lables_out gbt.simple.lables
$ paste gbt.simple.lables income.test.labels

72. 72
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
$ skytree-server gbt --testing_in income.test.st --model_in gbt.simple.model
--labels_out gbt.simple.lables --probabilities_out gbt.simple.pobs
$ less gbt.simple.pobs

73. 73
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
$ skytree-server gbt --testing_in income.test.st --model_in gbt.simple.model --probabilities_out
gbt.simple.probs --probability_threshold 0.8 --labels_out gbt.simple.thresh.0.8.labels
$ paste gbt.simple.thresh.0.8.labels gbt.simple.probs

74. 74
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
http://www.skytree.net/2016/02/05/skytree-express-machine-learning-at-the-
command-line/
Homework:

75. 75
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
What machine learning algorithms are available through Skytree CLI?
• AutoModel automodel
• Gradient-boosted decision trees gbt
• Random decision forests rdf
• The above for regression gbtr, rdfr
• Support vector machines (linear and nonlinear) svm
• Nearest neighbors binary classification nnc, with weights wnnc
• K-means clustering kmeans
• Logistic regression logistic
• Singular value decomposition svd (includes principal components analysis)
• Generalized linear model for classification or regression glmc, glmr
• Collaborative Filtering cf
• Kernel density estimation kde
• What-if analysis whatif
• Two-point correlation function two_pt
• Model scoring score

76. 76
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

77. 77
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

78. 78
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
18x18

79. 79
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

80. 80
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

81. 81
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

82. 82
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

83. 83
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

84. 84
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

85. 85
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

86. 86
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
18x18

87. 87
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
To feed an image into our neural network, we simply treat
the 18x18 pixel image as an array of 324 numbers:

88. 88
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
The handle 324 inputs, we’ll just enlarge our neural network to have 324 input nodes:

89. 89
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
All that’s left is to train the neural network with images of “8”s and not-“8"s so it learns to tell
them apart. When we feed in an “8”, we’ll tell it the probability the image is an “8” is 100% and
the probability it’s not an “8” is 0%.
Some training data:

90. 90
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
!‫ﮐﻨﯿﻢ‬ ‫ﺷﻨﺎﺳﺎﯾﯽ‬ ‫ﺭرﻭو‬ ‫ﺣﺮﻭوﻑف‬ ‫ﻣﯿﺘﻮﻧﯿﻢ‬ ‫ﻭو‬ ‫ﺷﺪﻩه‬ ‫ﺗﻤﻮﻡم‬ ‫ﮐﺎﺭر‬ ‫ﻇﺎﻫﺮﺍا‬

91. 91
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

92. 92
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
…‫ﻧﯿﺴﺖ‬ ‫‌ﻫﺎ‬‫ﯽ‬‫ﺳﺎﺩدﮔ‬ ‫ﺍاﯾﻦ‬ ‫ﺑﻪ‬ ‫ﻭوﻟﯽ‬
): ‫ﺍاﺳﺖ‬ ‫ﺣﺴﺎﺱس‬ ‫ﺣﺮﻭوﻑف‬ ‫ﻣﻮﻗﻌﯿﺖ‬ ‫ﺟﺎﺑﺠﺎﯾﯽ‬ ‫ﺑﻪ‬ ‫ﻣﺎ‬ ‫ﺭرﻭوﺵش‬

93. 93
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
(: ‫ﻣﯿﮑﻨﻪ‬ ‫ﮐﺎﺭر‬ … ‫ﺑﺎﺷﻪ‬ ‫ﺻﻔﺤﻪ‬ ‫ﻭوﺳﻂ‬ ‫ﺩدﺭرﺳﺖ‬ ‫ﻣﺎ‬ 8 ‫ﺍاﮔﺮ‬

94. 94
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

95. 95
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
…‫ﻧﯿﺴﺖ‬ ‫ﺷﺪﻩه‬ ‫ﮐﺸﯽ‬ ‌‫ﻂ‬‫ﺧ‬ ‫ﻭوﺍاﻗﻌﯽ‬ ‫ﺩدﻧﯿﺎﯼی‬
‫ﮐﻨﯿﻢ؟‬ ‫ﺷﻨﺎﺳﺎﯾﯽ‬ ‫ﻧﯿﺴﺖ‬ ‫ﺗﺼﻮﯾﺮ‬ ‫ﻭوﺳﻂ‬ ‫ﻭوﻗﺘﯽ‬ ‫ﺣﺘﯽ‬ ‫ﺭرﺍا‬ 8 ‫ﺗﺎ‬ ‫ﮐﻨﯿﻢ‬ ‫ﭼﻪ‬

96. 96
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
…‫‌ﮐﻨﯿﻢ‬‫ﯽ‬‫ﻣ‬ ‫ﺍاﺳﺘﻔﺎﺩدﻩه‬ ‫ﻣﺘﺤﺮﮎک‬ ‫ﭘﻨﺠﺮﻩه‬ ‫ﺍاﺯز‬

97. 97
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

98. 98
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

99. 99
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

100. 100
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

101. 101
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

102. 102
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

103. 103
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

104. 104
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

105. 105
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
This approach called a Sliding Window

106. 106
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﻮﺩد؟‬ ‫ﺧﻮﺑﯽ‬ ‫ﺭرﻭوﺵش‬
‫ﺑﻮﺩد؟‬ ‫ﮐﺎﺭرﺍاﻣﺪﯼی‬ ‫ﺭرﻭوﺵش‬ ‫ﻣﺘﺤﺮﮎک‬ ‫ﭘﻨﺠﺮﻩه‬ ‫ﺁآﯾﺎ‬
‫‌ﮐﻨﯿﺪ؟‬‫ﯽ‬‫ﻣ‬ ‫ﭘﯿﺸﻨﻬﺎﺩد‬ ‫ﺩدﯾﮕﺮﯼی‬ ‫ﺭرﻭوﺵش‬ ‫ﭼﻪ‬

107. 107
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

108. 108
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

109. 109
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬

110. 110
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
As a human, you intuitively know that pictures have a hierarchy or conceptual structure.

111. 111
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
As a human, you intuitively know that pictures have a hierarchy or conceptual structure.
As a human, you instantly recognize the hierarchy in this picture:
• The ground is covered in grass and concrete
• There is a child
• The child is sitting on a bouncy horse
• The bouncy horse is on top of the grass

112. 112
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
As a human, you intuitively know that pictures have a hierarchy or conceptual structure.
Mad scientists literally poking cat brains with weird probes
to figure out how cats process images

113. 113
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
How Convolution Works?

114. 114
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﻫﻤﭙﻮﺷﺎﻥن‬ ‫ﻗﻄﻌﺎﺗﯽ‬ ‫ﺑﻪ‬ ‫ﺗﺼﻮﯾﺮ‬ ‫ﺷﮑﺴﺘﻦ‬ .۱

115. 115
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺗﺼﻮﯾﺮ‬ ‫ﻗﻄﻌﺎﺕت‬ ‫ﺑﺎ‬ ‫ﮐﻮﭼﮏ‬ ‫‌ﺍاﯼی‬‫ﻪ‬‫ﺷﺒﮑ‬ ‫ﺗﻐﺬﯾﻪ‬ .۲

116. 116
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺟﺪﯾﺪ‬ ‫ﺍاﯼی‬ ‫ﺭرﺍاﯾﻪ‬ٰ‫ﺁآ‬ ‫ﺩدﺭر‬ ‫ﻗﻄﻌﻪ‬ ‫ﻫﺮ‬ ‫ﻧﺘﺎﯾﺞ‬ ‫ﺫذﺧﯿﺮﻩه‬ .۳

117. 117
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺟﺪﯾﺪ‬ ‫ﺍاﯼی‬ ‫ﺭرﺍاﯾﻪ‬ٰ‫ﺁآ‬ ‫ﺩدﺭر‬ ‫ﻗﻄﻌﻪ‬ ‫ﻫﺮ‬ ‫ﻧﺘﺎﯾﺞ‬ ‫ﺫذﺧﯿﺮﻩه‬ .۳

118. 118
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﻧﺘﺎﯾﺞ‬ ‫ﻣﺮﺣﻠﻪ‬ ‫ﻣﺎﺗﺮﯾﺲ‬ ‫ﺍاﻧﺪﺍاﺯزﻩه‬ ‫ﮐﺎﻫﺶ‬ .۴
Max Pooling

119. 119
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﻧﺘﺎﯾﺞ‬ ‫ﻣﺮﺣﻠﻪ‬ ‫ﻣﺎﺗﺮﯾﺲ‬ ‫ﺍاﻧﺪﺍاﺯزﻩه‬ ‫ﮐﺎﻫﺶ‬ .۴

120. 120
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﻣﺮﺣﻠﻪ‬ ‫ﺁآﺧﺮﯾﻦ‬ ‫ﻣﺘﺼﻞ‬ ‫ﺗﻤﺎﻡم‬ ‫۵.ﺷﺒﮑﻪ‬

121. 121
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﯿﺸﺘﺮ‬ ‫‌ﻫﺎﯼی‬‫ﻪ‬‫ﻻﯾ‬ ‫ﺍاﻓﺰﻭوﺩدﻥن‬
Our image processing pipeline is a series of steps:
• convolution,
• max-pooling,
• a fully-connected network.

122. 122
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﯿﺸﺘﺮ‬ ‫‌ﻫﺎﯼی‬‫ﻪ‬‫ﻻﯾ‬ ‫ﺍاﻓﺰﻭوﺩدﻥن‬
The first convolution step might learn to recognize sharp edges, the
second convolution step might recognize beaks using it’s knowledge
of sharp edges, the third step might recognize entire birds using it’s
knowledge of beaks, etc.
The more convolution steps you have, the more complicated
features your network will be able to learn to recognize.

123. 123
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﯿﺸﺘﺮ‬ ‫‌ﻫﺎﯼی‬‫ﻪ‬‫ﻻﯾ‬ ‫ﺍاﻓﺰﻭوﺩدﻥن‬
In this case, they start a 224 x 224 pixel image, apply convolution and max pooling
twice, apply convolution 3 more times, apply max pooling and then have two fully-
connected layers. The end result is that the image is classified into one of 1000
categories!

124. 124
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﮐﻨﯿﻢ؟‬ ‫ﺷﺮﻭوﻉع‬ ‫ﮐﺠﺎ‬ ‫ﺍاﺯز‬
So how do you know which steps you need to combine to make
your image classifier work?
Honestly, you have to answer this by doing a lot of experimentation and
testing. You might have to train 100 networks before you find the optimal
structure and parameters for the problem you are solving. Machine
learning involves a lot of trial and error!

125. 125
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫‌ﭘﺮﻧﺪﻩه‬‫ﺮ‬‫ﻏﯿ‬ ‫ﻭو‬ ‫ﭘﺮﻧﺪﻩه‬

126. 126
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﯿﺸﺘﺮ‬ ‫‌ﻫﺎﯼی‬‫ﻪ‬‫ﻻﯾ‬ ‫ﺍاﻓﺰﻭوﺩدﻥن‬

127. 127
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺁآﺳﯿﺎﯾﯽ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬

128. 128
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺁآﺳﯿﺎﯾﯽ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬

129. 129
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺁآﺳﯿﺎﯾﯽ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬

130. 130
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺁآﺳﯿﺎﯾﯽ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬

131. 131
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺁآﺳﯿﺎﯾﯽ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬

132. 132
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺁآﺳﯿﺎﯾﯽ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬

133. 133
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺁآﺳﯿﺎﯾﯽ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬

134. 134
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺁآﺳﯿﺎﯾﯽ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬

135. 135
‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﻣﺮﺟﻊ‬
http://www.slideshare.net/billlangjun/simple-introduction-to-autoencoder
http://deeplearningbook.org
http://www.skytree.net/2016/02/05/skytree-express-machine-learning-at-the-command-line/
http://www.datasciencecentral.com/profiles/blogs/must-know-tips-tricks-in-deep-neural-networks-1
https://www.ibm.com/developerworks/linux/library/l-machine-learning-deep-learning-trs/index.html?ca=drs-
&ce=ism0070&ct=is&cmp=ibmsocial&cm=h&cr=crossbrand&ccy=us
https://xkcd.com/1425/
http://yann.lecun.com/exdb/mnist/
https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-
f40359318721#.njwdy7ch4
https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer
Spark Summit 2016 - The Baidu Presentation