際際滷shows by User: khalooei / http://www.slideshare.net/images/logo.gif 際際滷shows by User: khalooei / Tue, 31 Dec 2019 18:03:54 GMT 際際滷Share feed for 際際滷shows by User: khalooei Robustness of Deep Neural Networks /slideshow/robustness-of-deep-neural-networks/213453786 khalooeiwss2019-191231180354
Nowadays, Deep neural networks are the most popular approach which we see its usage in different applications and tasks. As day growth its usage in different tasks, checking the vulnerability of these networks is being a very important fundamental issue. Therefore, analyzing of each machine learning model (such as neural network) for its vulnerability, is a useful task to assess the usage of that in critical situations. In this session, We try to cover the key definition step's of vulnerability of deep neural networks and its defense strategies against simplest vulnerability at first. Then when the minds are boiled, we try to implement and test them in a practical manner. Also, covering a teamwork remote session for more collaboration is available at the end of the session. (Winter Seminar Series - WSS - Mohammad Khalooei - Sharif University of Technology)]]>
Nowadays, Deep neural networks are the most popular approach which we see its usage in different applications and tasks. As day growth its usage in different tasks, checking the vulnerability of these networks is being a very important fundamental issue. Therefore, analyzing of each machine learning model (such as neural network) for its vulnerability, is a useful task to assess the usage of that in critical situations. In this session, We try to cover the key definition step's of vulnerability of deep neural networks and its defense strategies against simplest vulnerability at first. Then when the minds are boiled, we try to implement and test them in a practical manner. Also, covering a teamwork remote session for more collaboration is available at the end of the session. (Winter Seminar Series - WSS - Mohammad Khalooei - Sharif University of Technology)]]> Tue, 31 Dec 2019 18:03:54 GMT /slideshow/robustness-of-deep-neural-networks/213453786 khalooei@slideshare.net(khalooei) Robustness of Deep Neural Networks khalooei Nowadays, Deep neural networks are the most popular approach which we see its usage in different applications and tasks. As day growth its usage in different tasks, checking the vulnerability of these networks is being a very important fundamental issue. Therefore, analyzing of each machine learning model (such as neural network) for its vulnerability, is a useful task to assess the usage of that in critical situations. In this session, We try to cover the key definition step's of vulnerability of deep neural networks and its defense strategies against simplest vulnerability at first. Then when the minds are boiled, we try to implement and test them in a practical manner. Also, covering a teamwork remote session for more collaboration is available at the end of the session. (Winter Seminar Series - WSS - Mohammad Khalooei - Sharif University of Technology) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/khalooeiwss2019-191231180354-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Nowadays, Deep neural networks are the most popular approach which we see its usage in different applications and tasks. As day growth its usage in different tasks, checking the vulnerability of these networks is being a very important fundamental issue. Therefore, analyzing of each machine learning model (such as neural network) for its vulnerability, is a useful task to assess the usage of that in critical situations. In this session, We try to cover the key definition step&#39;s of vulnerability of deep neural networks and its defense strategies against simplest vulnerability at first. Then when the minds are boiled, we try to implement and test them in a practical manner. Also, covering a teamwork remote session for more collaboration is available at the end of the session. (Winter Seminar Series - WSS - Mohammad Khalooei - Sharif University of Technology)
Robustness of Deep Neural Networks from khalooei
]]> 301 1 https://cdn.slidesharecdn.com/ss_thumbnails/khalooeiwss2019-191231180354-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Robustness of Deep Neural Networks | Adversarial attacks and defenses /slideshow/robustness-of-deep-neural-networks-adversarial-attacks-and-defenses/158081132 khalooei-adversarialattack-aaiss-v2-190726123820
The presentation is presented by Mohammad Khalooei at Amirkabir Artificial Intelligence Summer Summit 2019 at Amirkabir University of Technology (Tehran Polytechnic). Different methodology to attack and defense against deep neural networks due to evaluate the robustness of DNN's is presented. For more informations, you can follow the presentations at https://ceit.aut.ac.ir/~khalooei/#presentations. ]]>
The presentation is presented by Mohammad Khalooei at Amirkabir Artificial Intelligence Summer Summit 2019 at Amirkabir University of Technology (Tehran Polytechnic). Different methodology to attack and defense against deep neural networks due to evaluate the robustness of DNN's is presented. For more informations, you can follow the presentations at https://ceit.aut.ac.ir/~khalooei/#presentations. ]]> Fri, 26 Jul 2019 12:38:20 GMT /slideshow/robustness-of-deep-neural-networks-adversarial-attacks-and-defenses/158081132 khalooei@slideshare.net(khalooei) Robustness of Deep Neural Networks | Adversarial attacks and defenses khalooei The presentation is presented by Mohammad Khalooei at Amirkabir Artificial Intelligence Summer Summit 2019 at Amirkabir University of Technology (Tehran Polytechnic). Different methodology to attack and defense against deep neural networks due to evaluate the robustness of DNN's is presented. For more informations, you can follow the presentations at https://ceit.aut.ac.ir/~khalooei/#presentations. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/khalooei-adversarialattack-aaiss-v2-190726123820-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The presentation is presented by Mohammad Khalooei at Amirkabir Artificial Intelligence Summer Summit 2019 at Amirkabir University of Technology (Tehran Polytechnic). Different methodology to attack and defense against deep neural networks due to evaluate the robustness of DNN&#39;s is presented. For more informations, you can follow the presentations at https://ceit.aut.ac.ir/~khalooei/#presentations.
Robustness of Deep Neural Networks | Adversarial attacks and defenses from khalooei
]]> 230 2 https://cdn.slidesharecdn.com/ss_thumbnails/khalooei-adversarialattack-aaiss-v2-190726123820-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Life of Points (Machine learning with Orange flavor) /slideshow/life-of-points-machine-learning-with-orange-flavor/135525974 slide-khalooei-life-of-points-190310201234
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.]]>
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.]]> Sun, 10 Mar 2019 20:12:34 GMT /slideshow/life-of-points-machine-learning-with-orange-flavor/135525974 khalooei@slideshare.net(khalooei) Life of Points (Machine learning with Orange flavor) khalooei Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slide-khalooei-life-of-points-190310201234-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Life of Points (Machine learning with Orange flavor) from khalooei
]]> 384 2 https://cdn.slidesharecdn.com/ss_thumbnails/slide-khalooei-life-of-points-190310201234-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Generative Adversarial Network /slideshow/generative-adversarial-network-135525708/135525708 last-version-wss-khalooei-190310200452
A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework [WikiPedia]. In this presentation, I try to cover the concepts of GAN and it's applications. This presentations was presented by Mohammad Khalooei in WSS 2018 (Winter Seminar Series) at Sharif University of Technology.]]>
A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework [WikiPedia]. In this presentation, I try to cover the concepts of GAN and it's applications. This presentations was presented by Mohammad Khalooei in WSS 2018 (Winter Seminar Series) at Sharif University of Technology.]]> Sun, 10 Mar 2019 20:04:51 GMT /slideshow/generative-adversarial-network-135525708/135525708 khalooei@slideshare.net(khalooei) Generative Adversarial Network khalooei A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework [WikiPedia]. In this presentation, I try to cover the concepts of GAN and it's applications. This presentations was presented by Mohammad Khalooei in WSS 2018 (Winter Seminar Series) at Sharif University of Technology. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/last-version-wss-khalooei-190310200452-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework [WikiPedia]. In this presentation, I try to cover the concepts of GAN and it&#39;s applications. This presentations was presented by Mohammad Khalooei in WSS 2018 (Winter Seminar Series) at Sharif University of Technology.
Generative Adversarial Network from khalooei
]]> 560 8 https://cdn.slidesharecdn.com/ss_thumbnails/last-version-wss-khalooei-190310200452-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Generative Adversarial Networks - (Applications) /slideshow/generative-adversarial-networks-applications/84931783 khalooei-part2-171225180919
Generative adversarial network presentation which presented by Mohammad khalooei on Friday, 22 December 2017 at Tehran. http://ceit.aut.ac.ir/~khalooei/ ]]>
Generative adversarial network presentation which presented by Mohammad khalooei on Friday, 22 December 2017 at Tehran. http://ceit.aut.ac.ir/~khalooei/ ]]> Mon, 25 Dec 2017 18:09:19 GMT /slideshow/generative-adversarial-networks-applications/84931783 khalooei@slideshare.net(khalooei) Generative Adversarial Networks - (Applications) khalooei Generative adversarial network presentation which presented by Mohammad khalooei on Friday, 22 December 2017 at Tehran. http://ceit.aut.ac.ir/~khalooei/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/khalooei-part2-171225180919-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Generative adversarial network presentation which presented by Mohammad khalooei on Friday, 22 December 2017 at Tehran. http://ceit.aut.ac.ir/~khalooei/
Generative Adversarial Networks - (Applications) from khalooei
]]> 562 5 https://cdn.slidesharecdn.com/ss_thumbnails/khalooei-part2-171225180919-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Generative Adversarial Networks - (Introduction) /slideshow/generative-adversarial-networks-part1-introduction/84931367 khalooei-part1-171225175809
Generative adversarial network presentation which presented by Mohammad khalooei on Friday, 22 December 2017 at Tehran. http://ceit.aut.ac.ir/~khalooei/]]>
Generative adversarial network presentation which presented by Mohammad khalooei on Friday, 22 December 2017 at Tehran. http://ceit.aut.ac.ir/~khalooei/]]> Mon, 25 Dec 2017 17:58:09 GMT /slideshow/generative-adversarial-networks-part1-introduction/84931367 khalooei@slideshare.net(khalooei) Generative Adversarial Networks - (Introduction) khalooei Generative adversarial network presentation which presented by Mohammad khalooei on Friday, 22 December 2017 at Tehran. http://ceit.aut.ac.ir/~khalooei/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/khalooei-part1-171225175809-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Generative adversarial network presentation which presented by Mohammad khalooei on Friday, 22 December 2017 at Tehran. http://ceit.aut.ac.ir/~khalooei/
Generative Adversarial Networks - (Introduction) from khalooei
]]> 315 5 https://cdn.slidesharecdn.com/ss_thumbnails/khalooei-part1-171225175809-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 惠忰 惡悋 惘擧惘惆 悋惆擯惘 惘 惡惘 惡愕惠惘 擧悋惆悋惆 (2) /slideshow/ss-70486910/70486910 lec02-bigdata-deeplearning-sharif-161228051513
悋愕悋惆悋 悋惘悋悧 悴愕 惆 "惠忰 惡悋 惘擧惘惆 悋惆擯惘 惘 惡惘 惡愕惠惘 擧悋惆悋惆" 7惆1395 惆惘 擧悋惘擯惘 惆悋惆悋 惺惴 惆悋愆擯悋 惶惺惠 愆惘 忰惆 悽悋悧 mkhalooei@gmail.com --------------------------------- Analysis of big data, with deep learning approach presentation slides that presented by Mohammad khalooei at Sharif University big data committee on 27 Dec 2016. ]]>
悋愕悋惆悋 悋惘悋悧 悴愕 惆 "惠忰 惡悋 惘擧惘惆 悋惆擯惘 惘 惡惘 惡愕惠惘 擧悋惆悋惆" 7惆1395 惆惘 擧悋惘擯惘 惆悋惆悋 惺惴 惆悋愆擯悋 惶惺惠 愆惘 忰惆 悽悋悧 mkhalooei@gmail.com --------------------------------- Analysis of big data, with deep learning approach presentation slides that presented by Mohammad khalooei at Sharif University big data committee on 27 Dec 2016. ]]> Wed, 28 Dec 2016 05:15:13 GMT /slideshow/ss-70486910/70486910 khalooei@slideshare.net(khalooei) 惠忰 惡悋 惘擧惘惆 悋惆擯惘 惘 惡惘 惡愕惠惘 擧悋惆悋惆 (2) khalooei 悋愕悋惆悋 悋惘悋悧 悴愕 惆 "惠忰 惡悋 惘擧惘惆 悋惆擯惘 惘 惡惘 惡愕惠惘 擧悋惆悋惆" 7惆1395 惆惘 擧悋惘擯惘 惆悋惆悋 惺惴 惆悋愆擯悋 惶惺惠 愆惘 忰惆 悽悋悧 mkhalooei@gmail.com --------------------------------- Analysis of big data, with deep learning approach presentation slides that presented by Mohammad khalooei at Sharif University big data committee on 27 Dec 2016. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lec02-bigdata-deeplearning-sharif-161228051513-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 悋愕悋惆悋 悋惘悋悧 悴愕 惆 &quot;惠忰 惡悋 惘擧惘惆 悋惆擯惘 惘 惡惘 惡愕惠惘 擧悋惆悋惆&quot; 7惆1395 惆惘 擧悋惘擯惘 惆悋惆悋 惺惴 惆悋愆擯悋 惶惺惠 愆惘 忰惆 悽悋悧 mkhalooei@gmail.com --------------------------------- Analysis of big data, with deep learning approach presentation slides that presented by Mohammad khalooei at Sharif University big data committee on 27 Dec 2016.
惠忰 惡悋 惘擧惘惆 悋惆擯惘 惘 惡惘 惡愕惠惘 擧悋惆悋惆 (2) from khalooei
]]> 5798 7 https://cdn.slidesharecdn.com/ss_thumbnails/lec02-bigdata-deeplearning-sharif-161228051513-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 惠忰 惡悋 惘擧惘惆 悋惆擯惘 惘 惡惘 惡愕惠惘 擧悋惆悋惆 /slideshow/analysis-of-big-data-with-deep-learning-approach/69923804 bigdata-deeplearning-sharif-161207173836
悋愕悋惆悋 悋惘悋悧 "惠忰 惡悋 惘擧惘惆 悋惆擯惘 惘 惡惘 惡愕惠惘 擧悋惆悋惆" 17 悛悵惘1395 惆惘 擧悋惘擯惘 惆悋惆悋 惺惴 惆悋愆擯悋 惶惺惠 愆惘 忰惆 悽悋悧 mkhalooei@gmail.com 悋惘悋悧 http://takhtesefid.org/watch?v=533955521829 --------------------------------- Analysis of big data, with deep learning approach presentation slides that presented by Mohammad khalooei at Sharif University big data committee on Dec 2016. ]]>
悋愕悋惆悋 悋惘悋悧 "惠忰 惡悋 惘擧惘惆 悋惆擯惘 惘 惡惘 惡愕惠惘 擧悋惆悋惆" 17 悛悵惘1395 惆惘 擧悋惘擯惘 惆悋惆悋 惺惴 惆悋愆擯悋 惶惺惠 愆惘 忰惆 悽悋悧 mkhalooei@gmail.com 悋惘悋悧 http://takhtesefid.org/watch?v=533955521829 --------------------------------- Analysis of big data, with deep learning approach presentation slides that presented by Mohammad khalooei at Sharif University big data committee on Dec 2016. ]]> Wed, 07 Dec 2016 17:38:36 GMT /slideshow/analysis-of-big-data-with-deep-learning-approach/69923804 khalooei@slideshare.net(khalooei) 惠忰 惡悋 惘擧惘惆 悋惆擯惘 惘 惡惘 惡愕惠惘 擧悋惆悋惆 khalooei 悋愕悋惆悋 悋惘悋悧 "惠忰 惡悋 惘擧惘惆 悋惆擯惘 惘 惡惘 惡愕惠惘 擧悋惆悋惆" 17 悛悵惘1395 惆惘 擧悋惘擯惘 惆悋惆悋 惺惴 惆悋愆擯悋 惶惺惠 愆惘 忰惆 悽悋悧 mkhalooei@gmail.com 悋惘悋悧 http://takhtesefid.org/watch?v=533955521829 --------------------------------- Analysis of big data, with deep learning approach presentation slides that presented by Mohammad khalooei at Sharif University big data committee on Dec 2016. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdata-deeplearning-sharif-161207173836-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 悋愕悋惆悋 悋惘悋悧 &quot;惠忰 惡悋 惘擧惘惆 悋惆擯惘 惘 惡惘 惡愕惠惘 擧悋惆悋惆&quot; 17 悛悵惘1395 惆惘 擧悋惘擯惘 惆悋惆悋 惺惴 惆悋愆擯悋 惶惺惠 愆惘 忰惆 悽悋悧 mkhalooei@gmail.com 悋惘悋悧 http://takhtesefid.org/watch?v=533955521829 --------------------------------- Analysis of big data, with deep learning approach presentation slides that presented by Mohammad khalooei at Sharif University big data committee on Dec 2016.
惠忰 惡悋 惘擧惘惆 悋惆擯惘 惘 惡惘 惡愕惠惘 擧悋惆悋惆 from khalooei
]]> 6352 5 https://cdn.slidesharecdn.com/ss_thumbnails/bigdata-deeplearning-sharif-161207173836-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/khalooeiwss2019-191231180354-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/robustness-of-deep-neural-networks/213453786 Robustness of Deep Neu... https://cdn.slidesharecdn.com/ss_thumbnails/khalooei-adversarialattack-aaiss-v2-190726123820-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/robustness-of-deep-neural-networks-adversarial-attacks-and-defenses/158081132 Robustness of Deep Neu... https://cdn.slidesharecdn.com/ss_thumbnails/slide-khalooei-life-of-points-190310201234-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/life-of-points-machine-learning-with-orange-flavor/135525974 Life of Points (Machin...