Big Data and Machine Learning Workshop - Day 5 @ UTACM

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

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

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‫ﭘﯿﺸﮕﻔﺘﺎﺭر‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

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(‫)ﮐﻠﺴﯿﻔﯿﮑﺸﻦ‬ ‫‌ﺑﻨﺪﯼی‬‫ﻪ‬‫ﻃﺒﻘ‬ - ‫ﭘﯿﺸﮕﻔﺘﺎﺭر‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

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‫ﺑﺮﮔﺮﺩدﯾﻢ‬ ‫ﻋﻘﺐ‬ ‫ﺑﻪ‬ ‫ﮐﻤﯽ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
:‫ﺷﺪﯾﻢ‬ ‫ﺁآﺷﻨﺎ‬ ‫ﺧﻄﯽ‬ ‫ﮐﻠﺴﯿﻔﺎﯾﺮﻫﺎﯼی‬ ‫ﺑﺎ‬ ‫ﻗﺒﻼ‬

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‫ﺑﺮﮔﺮﺩدﯾﻢ‬ ‫ﻋﻘﺐ‬ ‫ﺑﻪ‬ ‫ﮐﻤﯽ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

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‫ﮐﻠﺴﯿﻔﺎﯾﺮ‬ ‫ﺁآﻣﻮﺯزﺵش‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

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Softmax Function‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
[ 3.0 1.0 0.2 ]Scores:
Probabilities:

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‫‌ﮐﻨﯿﺪ‬‫ﺪ‬‫ﻫﻮﺷﻤﻨ‬ ‫ﺭرﺍا‬ ‫ﺧﻮﺩد‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺩدﻭوﺭرﺑﯿﻦ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

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‫‌ﮐﻨﯿﺪ‬‫ﺪ‬‫ﻫﻮﺷﻤﻨ‬ ‫ﺭرﺍا‬ ‫ﺧﻮﺩد‬ ‫ﻣﺎﺷﯿﻦ‬ ‫ﺩدﻭوﺭرﺑﯿﻦ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

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‫ﮐﻨﯿﺪ‬ ‫ﺭرﻧﮏ‬ ‫ﺭرﺍا‬ ‫ﺟﺴﺘﺠﻮ‬ ‫ﻣﻮﺗﻮﺭر‬ ‫ﻧﺘﺎﯾﺞ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

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‫ﮐﻨﯿﺪ‬ ‫ﺭرﻧﮏ‬ ‫ﺭرﺍا‬ ‫ﺟﺴﺘﺠﻮ‬ ‫ﻣﻮﺗﻮﺭر‬ ‫ﻧﺘﺎﯾﺞ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

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‫ﺁآﺯزﻣﺎﯾﺶ‬ ‫ﻭو‬ ‫ﺍاﻋﺘﺒﺎﺭرﺳﻨﺠﯽ‬ ،‫ﺁآﻣﻮﺯزﺵش‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

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‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺁآﺯزﻣﺎﯾﺶ‬ ‫ﻭو‬ ‫ﺍاﻋﺘﺒﺎﺭرﺳﻨﺠﯽ‬ ،‫ﺁآﻣﻮﺯزﺵش‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬

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‫ﺁآﻣﻮﺯزﺷﯽ‬ ‫ﺩدﺍاﺩدﻩهﻫﺎﯼی‬
‫ﻣﯽﺳﭙﺎﺭرﺩد‬ ‫ﺧﺎﻃﺮ‬ ‫ﺑﻪ‬ ‫ﺭرﺍا‬

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!‫ﮐﻨﻪ‬ ‫ﺣﻔﻂ‬ ‫‌ﺗﻮﻧﻪ‬‫ﯽ‬‫ﻧﻤ‬

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F1 ‫ﻭو‬ ‫ﺟﺎﻣﻌﯿﺖ‬ ،‫ﺩدﻗﺖ‬
F1 = 2 * ((precision * recall) / (precision + recall))

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‫ﻣﻘﺪﻣﻪ‬ - ‫ﮊژﺭرﻑف‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

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‫ﺗﻨﻬﺎ‬ ‫ﻗﺒﻼ‬ ‫ﮐﻪ‬ ‫ﺑﭙﺮﺩدﺍاﺯزﻧﺪ‬ ‫ﻣﺴﺎﺋﻠﯽ‬ ‫ﺣﻞ‬ ‫ﺑﻪ‬ ‫ﺗﺎ‬ ‫‌ﺳﺎﺯزﺩد‬‫ﯽ‬‫ﻣ‬ ‫ﻗﺎﺩدﺭر‬ ‫ﺭرﺍا‬ ‫‌ﻫﺎ‬‫ﺮ‬‫ﮐﺎﻣﭙﯿﻮﺗ‬ ‫ﮊژﺭرﻑف‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
.‫‌ﺷﺪ‬‫ﯽ‬‫ﻣ‬ ‫ﺣﻞ‬ ‫ﺍاﻧﺴﺎﻥن‬ ‫ﺗﻮﺳﻂ‬
‫ﻣﻘﺪﻣﻪ‬ - ‫ﮊژﺭرﻑف‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬

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.‫ﺩدﺍاﺭرﺩد‬ ‫ﻧﯿﺎﺯز‬ ‌‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫ﺯزﯾﺎﺩدﯼی‬ ‫ﺑﺴﯿﺎﺭر‬ ‫ﻣﻘﺎﺩدﯾﺮ‬ ‫ﺑﻪ‬ ‫ﮊژﺭرﻑف‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬ ‫ﻓﺮﺁآﯾﻨﺪ‬
‫ﻣﻘﺪﻣﻪ‬ - ‫ﮊژﺭرﻑف‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬

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‫‌ﮊژﺭرﻑف‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﺑﺮ‬ ‫ﭘﯿﺸﺮﻭو‬ ‫‌ﻫﺎﯼی‬ ‫ﮐﻤﭙﺎﻧﯽ‬ ‫ﺗﻤﺮﮐﺰ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

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DATA …‫ﻭوﺏب‬ ‫ﺑﺰﺭرﮔﺎﻥن‬ ‫ﻣﺸﺘﺮﮎک‬ ‫ﻓﺼﻞ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

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AI ‫ﺣﻮﺯزﻩه‬ ‫ﺩدﺭر‬ ‫ﺗﺤﻘﯿﻘﺎﺗﯽ‬ ‫ﻣﻨﺎﺑﻊ‬ ‫ﺍاﺧﺘﺼﺎﺹص‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﻪ‬ ‫ﺭرﺍا‬ AI ‫ﺣﻮﺯزﻩه‬ ‫ﺩدﺭر‬ ‫ﺗﺤﻘﯿﻘﺎﺕت‬ ‫ﺍاﻧﺴﺎﻧﯽ‬ ‫ﻭو‬ ‫ﻣﺎﻟﯽ‬ ‫ﻣﻨﺎﺑﻊ‬ ‫ﺍاﺯز‬ ‫ﺳﻬﻢ‬ ‫ﺑﯿﺸﺘﺮﯾﻦ‬ ‫ﮊژﺭرﻑف‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
.‫ﺍاﺳﺖ‬ ‫ﺩدﺍاﺩدﻩه‬ ‫ﺍاﺧﺘﺼﺎﺹص‬ ‫ﺧﻮﺩد‬

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AI ‫ﺣﻮﺯزﻩه‬ ‫ﺩدﺭر‬ ‫ﺗﺤﻘﯿﻘﺎﺗﯽ‬ ‫ﻣﻨﺎﺑﻊ‬ ‫ﺍاﺧﺘﺼﺎﺹص‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﻪ‬ ‫ﺭرﺍا‬ AI ‫ﺣﻮﺯزﻩه‬ ‫ﺩدﺭر‬ ‫ﺗﺤﻘﯿﻘﺎﺕت‬ ‫ﺍاﻧﺴﺎﻧﯽ‬ ‫ﻭو‬ ‫ﻣﺎﻟﯽ‬ ‫ﻣﻨﺎﺑﻊ‬ ‫ﺍاﺯز‬ ‫ﺳﻬﻢ‬ ‫ﺑﯿﺸﺘﺮﯾﻦ‬ ‫ﮊژﺭرﻑف‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬
.‫ﺍاﺳﺖ‬ ‫ﺩدﺍاﺩدﻩه‬ ‫ﺍاﺧﺘﺼﺎﺹص‬ ‫ﺧﻮﺩد‬
‫ﻗﺮﺍاﺭر‬ ‫ﻫﻢ‬ ‫ﮐﻨﺎﺭر‬ ‫ﺩدﺭر‬ ‫ﺗﻮﺍاﻣﺎ‬ ‫ﭘﯿﭽﯿﺪﻩه‬ ‫ﻣﺴﺎﺋﻞ‬ ‫ﻭو‬ ‫ﺩدﺍاﺩدﻩه‬ ‫ﺍاﺯز‬ ‫ﺯزﯾﺎﺩدﯼی‬ ‫ﺑﺴﯿﺎﺭر‬ ‫ﻣﻘﺎﺩدﯾﺮ‬ ‫ﻭوﻗﺘﯽ‬ ‫ﮐﻪ‬ ‫ﭼﺮﺍا‬
.‫‌ﮔﺬﺍاﺭرﺩد‬‫ﯽ‬‫ﻣ‬ ‫ﻧﻤﺎﯾﺶ‬ ‫ﺑﻪ‬ ‫ﺍاﻧﮕﯿﺰ‬ ‫ﺍاﻋﺠﺎﺏب‬ ‫‌ﻫﺎﯾﯽ‬‫ﯽ‬‫ﺗﻮﺍاﻧﺎﯾ‬ ‫ﮊژﺭرﻑف‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬ ،‫ﮔﯿﺮﻧﺪ‬

48
‫ﮊژﺭرﻑف‬ ‌‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﮐﺮ‬ ‫ﺑﺮﺧﯽ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

49
‫ﮊژﺭرﻑف‬ ‌‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﮐﺮ‬ ‫ﺑﺮﺧﯽ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

50
‫ﮊژﺭرﻑف‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬ ‫ﺑﺮﺍاﯼی‬ ‫ﻣﺴﺌﻠﻪ‬ ‫ﯾﮏ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Q
‫ﮐﻪ‬ ‫ﻣﯽﺷﻨﺎﺳﯿﺪ‬ ‫ﺧﻮﺩد‬ ‫ﺭرﻭوﺯزﻣﺮﻩه‬ ‫ﺯزﻧﺪﮔﯽ‬ ‫ﻭو‬ ‫ﭘﯿﺮﺍاﻣﻮﻥن‬ ‫ﺩدﻧﯿﺎﯼی‬ ‫ﺩدﺭر‬ ‫ﻣﺴﺌﻠﻪﺍاﯼی‬ ‫ﭼﻪ‬
‫ﮐﺮﺩد؟‬ ‫ﺍاﻗﺪﺍاﻡم‬ ‫ﺁآﻥن‬ ‫ﺣﻞ‬ ‫ﺑﺮﺍاﯼی‬ ‫ﮊژﺭرﻑف‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬ ‫ﮐﻤﮏ‬ ‫ﺑﻪ‬ ‫ﻣﯿﺘﻮﺍاﻥن‬

51
‫ﺳﺮﺁآﻏﺎﺯز‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Torsten Wiesel (left) and David H. Hubel (right)
In one experiment, done in 1959, they
inserted a microelectrode into the
primary visual cortex of an anesthetized
cat. They then projected patterns of light
and dark on a screen in front of the cat.
They found that some neurons ﬁred
rapidly when presented with lines at one
angle, while others responded best to
another angle. Some of these neurons
responded to light patterns and dark
patterns differently.

52
Hubel and Wiesel called these neurons
simple cells. Still other neurons, which
they termed complex cells, detected
edges regardless of where they were
placed in the receptive ﬁeld of the
neuron and could preferentially detect
motion in certain directions. These
studies showed how the visual system
constructs complex representations of
visual information from simple stimulus
features.

53

54
Torsten Wiesel (left) and David H. Hubel (right) https://www.youtube.com/watch?v=8VdFf3egwfg

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‫ﻣﻌﯿﻦ‬ ‫ﻣﺤﻞ‬ ‫ﻭو‬ ‫ﺯزﻭوﺍاﯾﺎ‬ ‫ﺩدﺭر‬ ‫ﺗﺼﺎﻭوﯾﺮ‬ ‫ﻭو‬ ‫ﺍاﺷﯿﺎ‬ ‫‌ﻫﺎﯼی‬‫ﻪ‬‫ﻟﺒ‬ ‫ﺑﻪ‬ ‫‌ﻫﺎ‬‫ﻥن‬‫ﻧﻮﺭرﻭو‬ ‫ﺍاﺯز‬ ‫ﻻﯾﻪ‬ ‫ﺍاﻭوﻟﯿﻦ‬
!‫ﻣﯿﺪﻩه‬ ‫ﻧﺸﻮﻥن‬ ‫ﺣﺴﺎﺳﯿﺖ‬

56
!‫ﻣﯿﺪﻩه‬ ‫ﻧﺸﻮﻥن‬ ‫ﺣﺴﺎﺳﯿﺖ‬ ‫ﻣﻌﯿﻦ‬ ‫ﺟﻬﺖ‬ ‫‌ﺩدﺭر‬‫ﺖ‬‫ﺣﺮﮐ‬ ‫ﺑﻪ‬ ‫‌ﻫﺎ‬‫ﻥن‬‫ﻧﺮﻭو‬ ‫ﺍاﺯز‬ ‫‌ﻫﺎﯾﯽ‬‫ﻪ‬‫ﻻﯾ‬

57
‫ﺩدﺍاﺩدﻩه‬ ‫ﺁآﻣﻮﺯزﺵش‬ ‫ﺣﺮﮐﺖ‬ ‫ﻭو‬ ‫ﺩدﺭرﻭوﻥن‬ ،‫‌ﻫﺎ‬‫ﻪ‬‫ﻟﺒ‬ ‫ﺩدﯾﺪﻥن‬ ‫ﺑﺮﺍاﯼی‬ ‫ﻣﻌﯿﻦ‬ ‫‌ﻫﺎﯼی‬‫ﺲ‬‫ﺳﯿﻨﺎﭘ‬ ‫ﺑﺎ‬ ‫‌ﻫﺎﯾﯽ‬‫ﻥن‬‫ﻧﺮﻭو‬ ‫ﭘﺲ‬
!‫‌ﺍاﻧﺪ‬‫ﻩه‬‫ﺷﺪ‬

58
‫ﺣﺴﺎﺳﯿﺖ‬ ‌(‫ﺣﺮﮐﺖ‬ ‫)ﺟﻬﺖ‬ ‫ﺣﺮﮐﺖ‬ ‫ﺑﻪ‬ ‫ﺑﺮﺧﯽ‬ ،‫ﺯزﺍاﻭوﯾﻪ‬ ‫ﺑﻪ‬ ‫ﺑﺮﺧﯽ‬ ،‫ﺩدﺍاﺭرﻩه‬ ‫ﻭوﺟﻮﺩد‬ ‫ﺳﻠﻮﻟﻬﺎ‬ ‫ﺍاﺯز‬ ‫ﻣﺮﺍاﺗﺒﯽ‬ ‫ﺳﻠﺴﻠﻪ‬
.‫ﻣﯿﺸﻪ‬ ‫ﺷﻨﺎﺳﺎﯾﯽ‬ ‫ﺳﺮﻋﺖ‬ ‫ﺑﻪ‬ ‫ﺍاﺷﯿﺎ‬ ‫ﻣﺤﯿﻂ‬ ‫ﺳﻄﺢ‬ ‫ﺗﺮﯾﻦ‬ ‫ﺳﺎﺩدﻩه‬ ‫ﺩدﺭر‬ .‫ﻣﯿﺪﻥن‬ ‫ﻧﺸﻮﻥن‬

59
‫ﺷﺮﻭوﻉع‬ ‫ﭘﺲ‬ ،‫ﺑﮕﯿﺮﻩه‬ ‫ﺑﻬﺮﻩه‬ ‫ﻣﻐﺰ‬ ‫ﺩدﺭر‬ (‫ﺍاﻟﮕﻮ‬ ‫)ﺗﺸﺨﯿﺺ‬ ‫ﺷﻨﺎﺳﺎﯾﯽ‬ ‫‌ﻫﺎﯼی‬‫ﺵش‬‫ﺭرﻭو‬ ‫ﺍاﺯز‬ ‫ﺑﻮﺩد‬ ‫ﻋﻼﻗﻤﻨﺪ‬ ‫ﺑﺸﺮ‬
.‫ﺷﺪﻧﺪ‬ ‫ﭘﺴﺘﺎﻧﺪﺍاﺭرﺍاﻥن‬ ‫ﺩدﺭر‬ ‫ﺍاﺷﯿﺎ‬ ‫ﺷﻨﺎﺧﺖ‬ ‫ﺭرﻭوﺵش‬ ‫ﻣﺪﻟﺴﺎﺯزﯼی‬ ‫ﺑﻪ‬

60
!‫ﺑﯿﻨﺎﯾﯽ‬ ‫ﺑﻪ‬ ‫ﮐﺮﺩدﻥن‬ ‫ﻭوﺻﻞ‬ ‫ﺭرﻭو‬ ‫ﺷﻨﻮﺍاﯾﯽ‬
‫ﺣﻮﺍاﺱس‬ ‫ﺑﻘﯿﻪ‬ ‫ﻭو‬ … ‫ﻻﻣﺴﻪ‬ ‫ﻭو‬

61
‫ﮊژﺭرﻑف‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬ ‫‌ﻫﺎﯼی‬‫ﺐ‬‫ﻧﺸﯿ‬ ‫ﻭو‬ ‫ﻓﺮﺍاﺯز‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Unsupervised

62
Unsupervised

63
Unsupervised

64
Unsupervised

65
Unsupervised
Geoff Hinton
2012

66
Unsupervised
Geoff Hinton
2012

67

68
60M Parameters
1000 Categories

69

70

71

72

73

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‫ﮊژﺭرﻑف‬ ‫ﻋﺼﺒﯽ‬ ‫ﺷﺒﮑﻪ‬ ‫ﺍاﻧﻮﺍاﻉع‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Feedforward (acyclic graphs)
* Autoencoders
* Denoising autoencoders
* Restricted Boltzmann machines (stacked, they form deep-belief networks)
Convolutional
— Deep convolutional networks are SOTA for images. There are many well known architectures, including
AlexNet and VGGNet.
— Convolutional networks usually involved a combination of convolutional layers as well as subsampling
and fully connected feedforward layers.
Recurrent
— These handle time series data especially well. They can be combined with convolutional networks to
generate captions for images.
* Long Short-Term Memory
* GRU
Recursive
— These handle natural language especially well
* Recursive autoencoders
* Recursive neural tensor networks

75
‫ﻋﺼﺒﯽ‬ ‫‌ﻫﺎﯼی‬‫ﻪ‬‫ﺷﺒﮑ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Neural networks are a set of algorithms, modeled loosely after the
human brain, that are designed to recognize patterns. They
interpret sensory data through a kind of machine perception,
labeling or clustering raw input. The patterns they recognize are
numerical, contained in vectors, into which all real-world data, be
it images, sound, text or time series, must be translated.

76
Neural networks help us cluster and classify. You can think of them as
a clustering and classiﬁcation layer on top of data you store and
manage

77
They help to group unlabeled data according to similarities among
the example inputs, and they classify data when they have a
labeled dataset to train on.

78
‫ﮊژﺭرﻑف‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬ ‫ﺍاﺯز‬ ‫ﭘﯿﺶ‬ ‫‌ﻫﺎﯾﯽ‬‫ﺶ‬‫ﭘﺮﺳ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Is my problem supervised or unsupervised? If supervised, is it a
classiﬁcation or regression problem?
Supervised learning has a teacher. That teacher takes the form of a training
set that establishes correlations between two types of data, your input and
your output. You may want to apply labels to images, for example. In this
classiﬁcation problem, your input is raw pixels, and your output is the name
of whatever’s in the picture.
In a regression example, you might teach a neural net how to predict
continuous values such as housing price based on an input like square-
footage.
Unsupervised learning, on the other hand, can help you detect similarities
and anomalies simply by analyzing unlabeled data. Unsupervised learning
has no teacher; it can be applied to use cases such as image search and
fraud detection.

79
If supervised, how many labels am I dealing with?
The more labels you need to apply accurately, the more computationally
intensive your problem will be. ImageNet has a training set with about 1000
classes; the Iris dataset has just 3.

80
How many features am I dealing with?
The more features you have, the more memory you’ll need. With images,
the features of the ﬁrst layer equal the number of pixels in the image. So
MNIST’s 28*28 pixel images have 784 features. In medical diagnostics,
you may be looking at 14 megapixels.

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Another way to ask that same question is: What is my architecture
Resnet, the Microsoft Research net that won the most recent ImageNet
competition, had 150 layers. All other things being equal, the more layers
you add, the more features you have to deal with, the more memory you
need. A dense layer in a multilayer perceptron (MLP) is a lot more feature
intensive than a convolutional layer. People use convolutional nets with
subsampling precisely because they get to aggressively prune the
features they’re computing.

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How am I going to tune my neural net?
Tuning neural nets is still something of a dark art for a lot of people. There
are a couple of ways to go about it. You can tune empirically, looking at
the f1 score of your net and then adjusting the hyperparameters. You can
tune with some degree of automation using tools like hyperparameter
optimization. And ﬁnally, you can rely on heuristics like a GUI, which will
show you exactly how quickly your error is decreasing, and what your
activation distribution looks like.

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A lot of research is being conducted on 1-4 GPUs. Enterprise solutions usually
require more and have to work with large CPU clusters as well.
Hardware: Will I be using GPUs, CPUs or both? Am I going to rely on a
single-system GPU or a distributed system?

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What kind of problems does deep learning solve?
spam or not_spam in an email ﬁlter, good_guy or bad_guy in fraud detection,
angry_customer or happy_customer in customer relationship management.

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‫ﻣﺜﺎﻝل‬ ‫ﭼﻨﺪ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Classiﬁcation
• Detect faces, identify people in images, recognize facial expressions
(angry, joyful)
• Identify objects in images (stop signs, pedestrians, lane markers…)
• Recognize gestures in video
• Detect voices, identify speakers, transcribe speech to text, recognize
sentiment in voices
• Classify text as spam (in emails), or fraudulent (in insurance claims);
recognize sentiment in text (customer feedback)

86
Classiﬁcation
• Detect faces, identify people in images, recognize facial expressions
(angry, joyful)
• Identify objects in images (stop signs, pedestrians, lane markers…)
• Recognize gestures in video
• Detect voices, identify speakers, transcribe speech to text, recognize
sentiment in voices
• Classify text as spam (in emails), or fraudulent (in insurance claims);
recognize sentiment in text (customer feedback)
Any labels that humans can generate, any outcomes you care about
and which correlate to data, can be used to train a neural network.

87
Clustering
• Search: Comparing documents, images or sounds to surface
similar items.
• Anomaly detection: The ﬂipside of detecting similarities is
detecting anomalies, or unusual behaviour. In many cases,
unusual behaviour correlates highly with things you want to
detect and prevent, such as fraud.

88
Predictive Analytics
• Hardware breakdowns (data centers, manufacturing, transport)
• Health breakdowns (strokes, heart attacks based on vital stats
and data from wearables)
• Customer churn (predicting the likelihood that a customer will
leave, based on web activity and metadata)
• Employee turnover (ditto, but for employees)

89
‫ﺍاﺑﺰﺍاﺭر‬ ‫ﯾﮏ‬ ‫ﻣﻌﺮﻓﯽ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Predictive Analytics
• Hardware breakdowns (data centers, manufacturing, transport)
• Health breakdowns (strokes, heart attacks based on vital stats
and data from wearables)
• Customer churn (predicting the likelihood that a customer will
leave, based on web activity and metadata)
• Employee turnover (ditto, but for employees)

90
‫ﺳﺎﺯزﯼی‬ ‫ﭘﯿﺎﺩدﻩه‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﻨﻮﯾﺴﯿﻢ‬ ‫ﮐﺪ‬

91
‫ﻣﺴﺌﻠﻪ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
‫ﺑﺘﻮﺍاﻧﺪ‬ ‫ﮐﻪ‬ ‫ﺑﻨﻮﯾﺴﯿﺪ‬ ‫ﺍاﭘﻠﯿﮑﯿﺸﻨﯽ‬
‫ﯾﮏ‬ ‫ﺩدﺭرﻭوﻥن‬ ‫ﻣﺘﺤﺮﮎک‬ ‫ﺍاﺷﮑﺎﻝل‬ ‫ﺍاﻧﻮﺍاﻉع‬
‫‌ﻫﺎﯼی‬‫ﮓ‬‫ﺭرﻧ‬ ‫ﺑﺎ‬ ‫ﮐﻪ‬ ‫ﺭرﺍا‬ ‫ﻓﯿﻠﻢ‬ ‫ﺳﺮﯼی‬
‫ﮔﻮﻧﺎﮔﻮﻥن‬ ‫ﺟﻬﺎﺕت‬ ‫ﺩدﺭر‬ ‫ﻭو‬ ‫ﻣﺘﻨﻮﻉع‬
.‫ﮐﻨﺪ‬ ‫ﺷﻨﺎﺳﺎﯾﯽ‬ ‫‌ﮐﻨﻨﺪ‬‫ﯽ‬‫ﻣ‬ ‫ﺣﺮﮐﺖ‬

92
‫ﺟﺎﻭوﺍا‬ ‫‌ﻧﻮﯾﺴﺎﻥن‬‫ﻪ‬‫ﺑﺮﻧﺎﻣ‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺯزﺑﺎﻥن‬ ‫ﯾﮏ‬ - ‫‌ﺣﻞ‬‫ﻩه‬‫ﺭرﺍا‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Deeplearning4j is a domain-specific language to configure deep
neural networks, which are made of multiple layers. Everything
starts with a MultiLayerConfiguration, which organizes those layers
and their hyperparameters.

93
‫ﺟﺎﻭوﺍا‬ ‫‌ﻧﻮﯾﺴﺎﻥن‬‫ﻪ‬‫ﺑﺮﻧﺎﻣ‬ ‫ﺑﺮﺍاﯼی‬ ‫ﺯزﺑﺎﻥن‬ ‫ﯾﮏ‬ - ‫‌ﺣﻞ‬‫ﻩه‬‫ﺭرﺍا‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Hyperparameters are variables that determine how a neural network
learns. They include:
• how many times to update the weights of the model
• how to initialize those weights
• which activation function to attach to the nodes
• which optimization algorithm to use
• how fast the model should learn

94
‫ﻻﯾﻪ‬ ‫ﭼﻨﺪ‬ ‫ﺷﺒﮑﻪ‬ ‫ﯾﮏ‬ ‫ﺳﺎﺯزﯼی‬ ‫ﺁآﻣﺎﺩدﻩه‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

95
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.iterations(100).layer(new RBM())
.nIn(784).nOut(10).list(4).hiddenLayerSizes(new int[]{500, 250, 200})
.override(new ClassifierOverride(3))
.build();
For creating a deep learning network in Deeplearning4j, the foundation is
the MultiLayerConfiguration constructor. Below are the parameters for this
configuration and the default settings.
A multilayer network will accept the same kinds of inputs as a single-layer
network. The multilayer network parameters are also typically the same as
their single-layer network counterparts.

96
.build();
hiddenLayerSizes: int[], number of nodes for the feed forward layer
• two layers format = new int[]{50} = initiate int array with 50 nodes
• ﬁve layers format = new int[]{32,20,40,32} = layer 1 is 32 nodes, layer 2 is
20 nodes, etc

97
.build();
list: int, number of layers; this function replicates your conﬁguration n times
and builds a layerwise conﬁguration
• do not include input in the layer count

98
.build();
http://deeplearning4j.org/doc/

99
‫‌ﺣﻞ‬‫ﻩه‬‫ﺭرﺍا‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Solution:
Combine convolutional, max pooling, dense (feed forward)
and recurrent (LSTM) layers to classify each frame of a
video (using a generated/synthetic video data set)
Speciﬁcally, each video contains a shape (randomly
selected: circles, squares, lines, arcs) which persist for
multiple frames (though move between frames) and may
leave the frame. Each video contains multiple shapes which
are shown for some random number of frames.

100
‫‌ﺣﻞ‬‫ﻩه‬‫ﺭرﺍا‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Solution:
Combine convolutional, max pooling, dense (feed forward)
and recurrent (LSTM) layers to classify each frame of a
video (using a generated/synthetic video data set)
Speciﬁcally, each video contains a shape (randomly
selected: circles, squares, lines, arcs) which persist for
multiple frames (though move between frames) and may
leave the frame. Each video contains multiple shapes which
are shown for some random number of frames.
IMP

101
‫ﻣﻠﺰﻭوﻣﺎﺕت‬ - ‫ﻧﻈﺎﺭرﺕت‬ ‫ﺑﺎ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Prerequisites:
• Windows, Linux or Mac
• Java 1.7
• Apache Maven 3

102
Maven ‫ﺗﻮﺳﻂ‬ ‫ﭘﺎﯾﻪ‬ ‫ﭘﺮﻭوﮊژﻩه‬ ‫ﺳﺎﺧﺖ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Create the Maven project:
mvn archetype:generate
-DarchetypeGroupId=org.apache.maven.archetypes
-DgroupId=ir.ac.ut.acm.recurrent.video
-DartifactId=VideoClassiﬁcation
-DinteractiveMode=false

103
Rename the default created App class to VideoClassiﬁcation
mv main/java/ir/ac/ut/acm/recurrent/video/App.java
main/java/ir/ac/ut/acm/recurrent/video/VideoClassiﬁcation.java

104
<properties>
<nd4j.version>0.4-rc3.8</nd4j.version>
<dl4j.version>0.4-rc3.8</dl4j.version>
<canova.version>0.0.0.14</canova.version>
</properties>

105
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-nlp</artifactId>
<version>${dl4j.version}</version>
</dependency>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-core</artifactId>
<version>${dl4j.version}</version>
</dependency>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-x86</artifactId>
<version>${nd4j.version}</version>
</dependency>
<dependency>
<artifactId>canova-nd4j-image</artifactId>
<groupId>org.nd4j</groupId>
<version>${canova.version}</version>
</dependency>

106
Add ُthe following build plugin:
<plugin>
<artifactId>maven-compiler-plugin</artifactId>
<conﬁguration>
<source>1.7</source>
<target>1.7</target>
</conﬁguration>
</plugin>

107
Add the jar plugin:
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-jar-plugin</artifactId>
<configuration>
<archive>
<manifest>
<addClasspath>true</addClasspath>
<mainClass>ir.ac.ut.acm.recurrent.video.VidoClassification</mainClass>
</manifest>
</archive>
</configuration>
</plugin>

108
‫ﮐﺪ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

109

110

111

112
‫-ﺍاﺟﺮﺍا‬ ‫ﻧﻈﺎﺭرﺕت‬ ‫ﺑﺎ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
Run the class by using the following command:
mvn compile
Dexec.mainClass="ir.ac.ut.acm.recurrent.video.VideoClassiﬁcation"

113
‫ﻧﺘﺎﯾﺞ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬

114
Total time: 03:04 h

115

116
Total time: 16:01 h

117
‫ﻣﺨﺰﻥن‬ - ‫ﻧﻈﺎﺭرﺕت‬ ‫ﺑﺎ‬ ‫ﯾﺎﺩدﮔﯿﺮﯼی‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/
src/main/java/org/deeplearning4j/examples/recurrent/video/
VideoClassiﬁcationExample.java

118
‫ﻣﺮﺟﻊ‬‫ﺍاﻥن‬‫ﺮ‬‫ﺗﻬ‬ ‫ﺩدﺍاﻧﺸﮕﺎﻩه‬ ACM - ۱۳۹۵ ‫ﺗﺎﺑﺴﺘﺎﻥن‬ - ‫‌ﻣﺎﺷﯿﻦ‬‫ﯼی‬‫ﯾﺎﺩدﮔﯿﺮ‬ ‫ﻭو‬ ‫ﺑﺰﺭرﮒگ‬ ‫‌ﻫﺎﯼی‬‫ﻩه‬‫ﺩدﺍاﺩد‬ ‫‌ﻫﺎﯼی‬‫ﺩد‬‫ﮐﺎﺭرﺑﺮ‬ ‫ﺑﺮ‬ ‫ﮔﺬﺭرﯼی‬
https://research.googleblog.com/2015/07/how-google-translate-squeezes-deep.html
http://deeplearning.net/tutorial/lenet.html
https://en.wikipedia.org/wiki/David_H._Hubel
https://www.youtube.com/watch?v=8VdFf3egwfg
https://groups.google.com/forum/#!msg/irandeeplearning/mRn5mSmN7jg/1dVeViynAAAJ
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artiﬁcial-intelligence-fall-2010/
http://cs231n.github.io/
http://deeplearningbook.org/
MIT Tech Review, 10 BREAKTHROUGH TECHNOLOGIES, 2013 - Robert D Hof

Big Data and Machine Learning Workshop - Day 5 @ UTACM

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Big Data and Machine Learning Workshop - Day 5 @ UTACM