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Big Data and Machine Learning Workshop - Day 6 @ UTACM

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Big Data and Machine Learning Workshop - Day 6 @ UTACM

  1. 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. 2. ‫ﺻﺪﯾﻘﯽ‬ ‫ﺍاﻣﯿﺮ‬ :‫ﻣﻮﺳﺲ‬ 2 ‫ﻣﻌﺮﻓﯽ‬ http://recommender.ir http://helio.ir http://commentum.ir @amirsedighi :‫ﺗﻮ4ﺘﺮ‬ :‫ﺍاﯾﻤﯿﻞ‬sedighi@gmail.com
  3. 3. 3 ‫‌ﻫﺎ‬‫ﺮ‬‫ﮐﺎﻣﭙﯿﻮﺗ‬ ‫ﺗﮑﺎﻣﻞ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬
  4. 4. 4 ‫ﺍاﻧﺴﺎﻥن‬ ‫ﺩدﺍاﻧﺶ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬
  5. 5. 5 ‫ﺍاﻧﺴﺎﻥن‬ ‫ﺩدﺍاﻧﺶ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬
  6. 6. 6 ‫ﺍاﻧﺴﺎﻥن‬ ‫ﺩدﺍاﻧﺶ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬ ‫ﺫذﻫﻨﯽ‬ ‫ﻭو‬ ‫ﺑﺼﺮﯼی‬ subjective and intuitive ‫‌ﻧﺪﺍاﺭرﺩد‬ ‫ﻓﺮﻣﻮﻝل‬
  7. 7. 7 ‫ﺍاﻧﺴﺎﻥن‬ ‫ﺩدﺍاﻧﺶ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬ ‫ﺫذﻫﻨﯽ‬ ‫ﻭو‬ ‫ﺑﺼﺮﯼی‬ subjective and intuitive ‫‌ﻧﺪﺍاﺭرﺩد‬ ‫ﻓﺮﻣﻮﻝل‬ ‫ﻣﺎ‬ ‫ﻣﺜﻞ‬
  8. 8. 8 ‫ﺍاﻧﺴﺎﻥن‬ ‫ﺩدﺍاﻧﺶ‬ - ‫ﻗﺒﻞ‬ ‫ﺟﻠﺴﺎﺕت‬ ‫ﺑﺮ‬ ‫ﻣﺮﻭوﺭرﯼی‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
  9. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 22 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ Factors of Variation Factors of Variation: Position
  23. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 33 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬
  34. 34. 34 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬ Car Person Animal
  35. 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. 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. 37 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬
  38. 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. 39 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫‌ﻫﺎﯼی‬ ‫ﻻﯾﻪ‬ ‫ﻣﺨﻔﯽ‬ ‫‌ﺑﺎﺯزﻧﻤﺎﯾﯽ‬ + ‫‌ﺳﺎﺯزﯼی‬‫ﻩه‬‫ﺳﺎﺩد‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺗﮑﻨﯿﮏ‬ ‫ﯾﮏ‬
  40. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 50 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ Autoencoder
  51. 51. 51 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺟﺎﻭوﺍا‬ ‫ﺩدﺭر‬ ‫ﮐﺪ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬
  52. 52. 52 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺟﺎﻭوﺍا‬ ‫ﺩدﺭر‬ ‫ﮐﺪ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬
  53. 53. 53 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺟﺎﻭوﺍا‬ ‫ﺩدﺭر‬ ‫ﮐﺪ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ Encoder Decoder
  54. 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. 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. 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. 57 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
  58. 58. 58 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
  59. 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. 60 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
  61. 61. 61 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
  62. 62. 62 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
  63. 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. 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. 65 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
  66. 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. 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. 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. 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. 70 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﺑﺎ‬ ‫ﺁآﺷﻨﺎﯾﯽ‬ - ‫ﻣﺎﺷﯿﻦ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
  71. 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. 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. 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. 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. 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. 76 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  77. 77. 77 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  78. 78. 78 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬ 18x18
  79. 79. 79 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  80. 80. 80 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  81. 81. 81 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  82. 82. 82 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  83. 83. 83 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  84. 84. 84 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  85. 85. 85 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  86. 86. 86 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬ 18x18
  87. 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. 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. 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. 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. 91 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  92. 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. 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. 94 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  95. 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. 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. 97 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  98. 98. 98 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  99. 99. 99 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  100. 100. 100 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  101. 101. 101 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  102. 102. 102 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  103. 103. 103 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  104. 104. 104 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  105. 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. 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. 107 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  108. 108. 108 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  109. 109. 109 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬
  110. 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. 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. 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. 113 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﺪﻫﯿﻢ‬ ‫ﯾﺎﺩد‬ ‫ﺧﻮﺍاﻧﺪﻥن‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺑﻪ‬ How Convolution Works?
  114. 114. 114 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﻫﻤﭙﻮﺷﺎﻥن‬ ‫ﻗﻄﻌﺎﺗﯽ‬ ‫ﺑﻪ‬ ‫ﺗﺼﻮﯾﺮ‬ ‫ﺷﮑﺴﺘﻦ‬ .۱
  115. 115. 115 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺗﺼﻮﯾﺮ‬ ‫ﻗﻄﻌﺎﺕت‬ ‫ﺑﺎ‬ ‫ﮐﻮﭼﮏ‬ ‫‌ﺍاﯼی‬‫ﻪ‬‫ﺷﺒﮑ‬ ‫ﺗﻐﺬﯾﻪ‬ .۲
  116. 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. 117 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺟﺪﯾﺪ‬ ‫ﺍاﯼی‬ ‫ﺭرﺍاﯾﻪ‬ٰ‫ﺁآ‬ ‫ﺩدﺭر‬ ‫ﻗﻄﻌﻪ‬ ‫ﻫﺮ‬ ‫ﻧﺘﺎﯾﺞ‬ ‫ﺫذﺧﯿﺮﻩه‬ .۳
  118. 118. 118 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﻧﺘﺎﯾﺞ‬ ‫ﻣﺮﺣﻠﻪ‬ ‫ﻣﺎﺗﺮﯾﺲ‬ ‫ﺍاﻧﺪﺍاﺯزﻩه‬ ‫ﮐﺎﻫﺶ‬ .۴ Max Pooling
  119. 119. 119 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﻧﺘﺎﯾﺞ‬ ‫ﻣﺮﺣﻠﻪ‬ ‫ﻣﺎﺗﺮﯾﺲ‬ ‫ﺍاﻧﺪﺍاﺯزﻩه‬ ‫ﮐﺎﻫﺶ‬ .۴
  120. 120. 120 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﻣﺮﺣﻠﻪ‬ ‫ﺁآﺧﺮﯾﻦ‬ ‫ﻣﺘﺼﻞ‬ ‫ﺗﻤﺎﻡم‬ ‫۵.ﺷﺒﮑﻪ‬
  121. 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. 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. 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. 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. 125 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫‌ﭘﺮﻧﺪﻩه‬‫ﺮ‬‫ﻏﯿ‬ ‫ﻭو‬ ‫ﭘﺮﻧﺪﻩه‬
  126. 126. 126 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺑﯿﺸﺘﺮ‬ ‫‌ﻫﺎﯼی‬‫ﻪ‬‫ﻻﯾ‬ ‫ﺍاﻓﺰﻭوﺩدﻥن‬
  127. 127. 127 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺁآﺳﯿﺎﯾﯽ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬
  128. 128. 128 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺁآﺳﯿﺎﯾﯽ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬
  129. 129. 129 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺁآﺳﯿﺎﯾﯽ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬
  130. 130. 130 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺁآﺳﯿﺎﯾﯽ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬
  131. 131. 131 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺁآﺳﯿﺎﯾﯽ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬
  132. 132. 132 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺁآﺳﯿﺎﯾﯽ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬
  133. 133. 133 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺁآﺳﯿﺎﯾﯽ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬
  134. 134. 134 ‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬ ‫ﺁآﺳﯿﺎﯾﯽ‬ ‫ﻧﻤﻮﻧﻪ‬ ‫ﯾﮏ‬ ‫ﺩدﺭر‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬
  135. 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

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