ArtificialIntelligenceArticles
CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms #MachineLearning #DeepLearning https://arxiv.org/abs/1706.07068
Futurism
AI Can Now Produce Better Art Than Humans. Here's How.
Scientists have created an artificially intelligent system that is capable of producing cutting edge paintings that are better than works created by humans.
A Computer That Reads Body Language Real-time detector understands hand gestures, tracks multiple people https://www.techexplorist.com/computer-reads-body-language/
#مقاله
🌒Deep convolution neural networks allow analysis of cell motility #CNN #neuralnetworks #deeplearning #cells
http://www.biorxiv.org/content/early/2017/07/05/159202
https://yangx.top/ArtificialIntelligenceArticles
🌒Deep convolution neural networks allow analysis of cell motility #CNN #neuralnetworks #deeplearning #cells
http://www.biorxiv.org/content/early/2017/07/05/159202
https://yangx.top/ArtificialIntelligenceArticles
bioRxiv
Deep convolutional neural networks allow analysis of cell motility during stem cell differentiation and neoplastic transformation
Cells in culture display diverse motility behaviors. In multiple contexts, motility behaviors reflect broader cell function, providing motivation to discriminate between different motility behaviors. Current methods to do so rely upon manual feature engineering.…
🔵Shogi: A measure of artificial intelligence
http://www.japantimes.co.jp/news/2017/07/08/national/media-national/shogi-measure-artificial-intelligence/#.WWFE3YSGPIU
http://www.japantimes.co.jp/news/2017/07/08/national/media-national/shogi-measure-artificial-intelligence/#.WWFE3YSGPIU
The Japan Times
Shogi: A measure of artificial intelligence
Though last Sunday's Tokyo assembly elections garnered the most media attention, another contest came in a close second, even if only two people were invol
Gating mass cytometry data by deep learning. #BigData #DeepLearning #MachineLearning #DataScience #AI
http://www.biorxiv.org/content/early/2017/03/27/054411
http://www.biorxiv.org/content/early/2017/03/27/054411
An Off-Policy Method For Reinforcement Learning. #BigData #MachineLearning #DataScience #AI
http://buff.ly/2tB0otf
http://buff.ly/2tB0otf
Machine Learning for medicine. #BigData #DeepLearning #MachineLearning #DataScience #AI #Python #HealthTech #ECG
http://buff.ly/2sYYosW
http://buff.ly/2sYYosW
A Hybrid FPGA-Based System for EEG Prediction. #BigData #MachineLearning #DataScience #AI #HealthTech #FPGAs #EEG
http://buff.ly/2ttIsCx
http://buff.ly/2ttIsCx
Editorial in Science on AI & its influences now avail: http://bit.ly/2uR51Pb
#خبر
Google’s Approach To Artificial Intelligence Machine Learning: Peter Norvig
http://artificialbrain.xyz/googles-approach-to-artificial-intelligence-machine-learning-peter-norvig/
Google’s Approach To Artificial Intelligence Machine Learning: Peter Norvig
http://artificialbrain.xyz/googles-approach-to-artificial-intelligence-machine-learning-peter-norvig/
“PyTorch, Dynamic Computational Graphs and Modular Deep Learning” https://medium.com/intuitionmachine/pytorch-dynamic-computational-graphs-and-modular-deep-learning-7e7f89f18d1
Learn to Track: Deep Learning for Tractography. http://www.biorxiv.org/content/early/2017/06/06/146688 #BigData #DeepLearning #MachineLearning #DataScience #AI
Decoding Emotional States in the Human #Brain. #BigData #MachineLearning #DataScience #AI #NeuroScience
http://buff.ly/2u4lT8Z
http://buff.ly/2u4lT8Z
Reimagining Machine Learning in hardware. #BigData #DeepLearning #MachineLearning #DataScience #AI
http://buff.ly/2sZR0gV
http://buff.ly/2sZR0gV
Deep Learning Models for Genomics. #BigData #DeepLearning #MachineLearning #DataScience #AI #Genomics #HealthTech
http://buff.ly/2uD9F47
http://buff.ly/2uD9F47
(http://axnegar.fahares.com/axnegar/OwA4kjZBlHYbzw/5067016.jpg)
#معرفی کتاب_جدید در مورد یادگیری عمیق برای بیومتریک ها
🔵Book Memo: “Deep Learning for Biometrics.
This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also examined.
” #BigData #DeepLearning #MachineLearning #DataScience #AI #Books
http://buff.ly/2ttRdMK
#معرفی کتاب_جدید در مورد یادگیری عمیق برای بیومتریک ها
🔵Book Memo: “Deep Learning for Biometrics.
This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also examined.
” #BigData #DeepLearning #MachineLearning #DataScience #AI #Books
http://buff.ly/2ttRdMK
Explainable #AI = #MachineLearning techniques that produce more understandable models: http://www.darpa.mil/program/explainable-artificial-intelligence … #BigData #DataScience #xAI
Loving the Artificial Intelligence Dress is a no brainer! The pattern reveals what happens inside machine learning: https://shenovafashion.com/blogs/blog/new-artificial-intelligence-dress-design
(http://axnegar.fahares.com/axnegar/Nat7Dey9XgEHb1/5071799.jpg)
👁🗨#معرفی_محقیق_هوش_مصنوعی
#معرفی_محققین_هوش_مصنوعی_در_جهان
شما احتمالا می دانید که هوش مصنوعی چیست و همچنین می دانید در همه ی زمینه ها کاربرد دارد .اما ممکن است شما با محققان و تکنولوژیست های هوش مصنوعی آشنایی نداشته باشید.در این کانال قصد داریم با معرفی محققین و چهره های هوش مصنوعی ایران و جهان در کانال بپردازیم همراه ما باشید.
امروز محقق یا فرد دیگه ای که در زمینه ی هوش مصنوعی فعالیت میکنند اشنا می شویم .قبلا با ANDREW NG ,Andrej Karpathy اشنا شدیم .
3.Michael I. Jordan
طبق گفته سایت FastML مایکل جردن یک متخصص مشهور از برکلی است.مایکل در مدل های گرافیکی احتمالی، روش های طیفی، پردازش زبان طبیعی، ژنتیک آماری بیشتر تمرکز می کند.او همچنین یکی از افرادی است که برای اولین بار روش هایی را که آمار و یادگیری ماشین با هم همپوشانی می کنند به ارمغان آورد.
🌎🔥اندرو نگ (ANDREW NG ) خود در دنیای یادگیری ماشین مشهور است یکی از شاگرادن مایکل جردن است .
🔵Biography
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley.
His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.
وب سایت شخصی :
https://people.eecs.berkeley.edu/~jordan/?_ga=1.104786372.57480868.1488216728
https://www2.eecs.berkeley.edu/Faculty/Homepages/jordan.html
محقق مایکل جردن برنده 100،000 دلار جایزه رومانهارت برای علم شناختی
http://news.berkeley.edu/2014/07/28/michael-jordan-wins-rumelhart-prize/
مقالات
https://scholar.google.com/citations?user=yxUduqMAAAAJ
https://www.researchgate.net/profile/Michael_Jordan13
مصاحبه ای با مایکل جردن
https://www.reddit.com/r/MachineLearning/comments/2fxi6v/ama_michael_i_jordan/
http://cmsa.fas.harvard.edu/bigdata_michaeljordan/
🔵On Computational Thinking, Inferential Thinking and Data Science
https://www.youtube.com/watch?v=bQ02K0kWKzg
🔵DEI Distinguished Lecturer Series - Michael I. Jordan
https://www.youtube.com/watch?v=Cq6xGNcYe-8
https://www.oreilly.com/ideas/ray-a-distributed-execution-framework-for-emerging-ai-applications
👁🗨#معرفی_محقیق_هوش_مصنوعی
#معرفی_محققین_هوش_مصنوعی_در_جهان
شما احتمالا می دانید که هوش مصنوعی چیست و همچنین می دانید در همه ی زمینه ها کاربرد دارد .اما ممکن است شما با محققان و تکنولوژیست های هوش مصنوعی آشنایی نداشته باشید.در این کانال قصد داریم با معرفی محققین و چهره های هوش مصنوعی ایران و جهان در کانال بپردازیم همراه ما باشید.
امروز محقق یا فرد دیگه ای که در زمینه ی هوش مصنوعی فعالیت میکنند اشنا می شویم .قبلا با ANDREW NG ,Andrej Karpathy اشنا شدیم .
3.Michael I. Jordan
طبق گفته سایت FastML مایکل جردن یک متخصص مشهور از برکلی است.مایکل در مدل های گرافیکی احتمالی، روش های طیفی، پردازش زبان طبیعی، ژنتیک آماری بیشتر تمرکز می کند.او همچنین یکی از افرادی است که برای اولین بار روش هایی را که آمار و یادگیری ماشین با هم همپوشانی می کنند به ارمغان آورد.
🌎🔥اندرو نگ (ANDREW NG ) خود در دنیای یادگیری ماشین مشهور است یکی از شاگرادن مایکل جردن است .
🔵Biography
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley.
His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.
وب سایت شخصی :
https://people.eecs.berkeley.edu/~jordan/?_ga=1.104786372.57480868.1488216728
https://www2.eecs.berkeley.edu/Faculty/Homepages/jordan.html
محقق مایکل جردن برنده 100،000 دلار جایزه رومانهارت برای علم شناختی
http://news.berkeley.edu/2014/07/28/michael-jordan-wins-rumelhart-prize/
مقالات
https://scholar.google.com/citations?user=yxUduqMAAAAJ
https://www.researchgate.net/profile/Michael_Jordan13
مصاحبه ای با مایکل جردن
https://www.reddit.com/r/MachineLearning/comments/2fxi6v/ama_michael_i_jordan/
http://cmsa.fas.harvard.edu/bigdata_michaeljordan/
🔵On Computational Thinking, Inferential Thinking and Data Science
https://www.youtube.com/watch?v=bQ02K0kWKzg
🔵DEI Distinguished Lecturer Series - Michael I. Jordan
https://www.youtube.com/watch?v=Cq6xGNcYe-8
https://www.oreilly.com/ideas/ray-a-distributed-execution-framework-for-emerging-ai-applications
Brain inspired Chips - Neuromorphic Computing
#AI #BigData #wearables #DataScience
#fintech #Insurtech
http://bit.ly/2sDgFwA
#AI #BigData #wearables #DataScience
#fintech #Insurtech
http://bit.ly/2sDgFwA