💻 Machine Learning Course
Advanced quantitative techniques to analyze data where humans fall short.
https://brilliant.org/courses/machine-learning/
About this course
Machine learning swoops in where humans fail — such as when there are three hundred variables to keep track of and thousands of elements to process. This course develops the mathematical basis needed to truly understand how problems of classification and estimation work.
By the end of this course, you’ll develop the techniques needed to analyze data and apply these techniques to real-world problems.
Advanced quantitative techniques to analyze data where humans fall short.
https://brilliant.org/courses/machine-learning/
About this course
Machine learning swoops in where humans fail — such as when there are three hundred variables to keep track of and thousands of elements to process. This course develops the mathematical basis needed to truly understand how problems of classification and estimation work.
By the end of this course, you’ll develop the techniques needed to analyze data and apply these techniques to real-world problems.
💡 Artificial Neural Networks Course
A quick dive into a cutting-edge computational method for learning.
https://brilliant.org/courses/artificial-neural-networks/
About this course
This course dives into the fundamentals of artificial neural networks, from the math to the basic models to applications and more complicated models. You’ll answer questions such as how a computer can distinguish between pictures of dogs and cats and learn to play great chess.
Using some inspiration from the human brain, linear algebra, and a bit of calculus, by the end of this course, you’ll gain an intuition for why these models work - not just a bunch of formulas.
A quick dive into a cutting-edge computational method for learning.
https://brilliant.org/courses/artificial-neural-networks/
About this course
This course dives into the fundamentals of artificial neural networks, from the math to the basic models to applications and more complicated models. You’ll answer questions such as how a computer can distinguish between pictures of dogs and cats and learn to play great chess.
Using some inspiration from the human brain, linear algebra, and a bit of calculus, by the end of this course, you’ll gain an intuition for why these models work - not just a bunch of formulas.
🔹 "Dynamical systems, attractors, and neural circuits" - a review of how dynamical systems theory is applied in neuroscience by Paul Miller
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4930057/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4930057/
PubMed Central (PMC)
Dynamical systems, attractors, and neural circuits
Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular…
#سمینارهای_هفتگی گروه سیستمهای پیچیده و علم شبکه دانشگاه شهید بهشتی
🔹شنبه، ۵ خرداد، ساعت ۱۵:۱۰- کلاس ۴، طبقه سوم دانشکده فیزیک، دانشگاه شهید بهشتی
@carimi
🔹شنبه، ۵ خرداد، ساعت ۱۵:۱۰- کلاس ۴، طبقه سوم دانشکده فیزیک، دانشگاه شهید بهشتی
@carimi
🔹How a Pioneer of Machine Learning Became One of Its Sharpest Critics
Judea Pearl helped artificial intelligence gain a strong grasp on probability, but laments that it still can't compute cause and effect.
https://www.theatlantic.com/amp/article/560675/
Judea Pearl helped artificial intelligence gain a strong grasp on probability, but laments that it still can't compute cause and effect.
https://www.theatlantic.com/amp/article/560675/
Apply now for the 2018 JSMF Postdoctoral Fellowship in Understanding Dynamic & Multi-scale Systems. 2-3 yrs & freedom to choose where to pursue training. Deadline is June 15. https://t.co/5qTC0zaAJk
دومین سمپوزیوم تازه های نقشه برداری مغز ایران (ISBM2018)
http://nbml.ir/ISBM2018
http://nbml.ir/ISBM2018
🌐 "The atom is the icon of the 20th century. The atom whirls alone. It is the metaphor for individuality. But the atom is the past. The symbol for the next century is the net."
Kevin Kelly
Kevin Kelly
Applications are now open for our awesome new international winter school on complex networks. Check out the 2018 Complex Networks Winter Workshop here https://t.co/Xc6lVcF5kJ https://t.co/YxF7gLTilq
Forwarded from IPM Data Science
مدرسه تابستانی علم داده (مقدماتی)
🔵 پژوهشگاه دانشهای بنیادی با همکاری مرکز علوم داده آمستردام
🕖 9 تا 14 تیر 1397
📍ثبت نام و اطلاعات بیشتر در سایت
conf.ipm.ir/elementary-school
@IPMDataScience
🔵 پژوهشگاه دانشهای بنیادی با همکاری مرکز علوم داده آمستردام
🕖 9 تا 14 تیر 1397
📍ثبت نام و اطلاعات بیشتر در سایت
conf.ipm.ir/elementary-school
@IPMDataScience
🎞ٰٰ در این ویدیو خیلی سریع با #پایتون آشنا میشین! همینطور مرور خیلی خوبی هست برای کسانی که قبلترها دستشون به برنامهنویسی رفته و الان دنبال یه بهونه خوب برای شروع برنامه نویسی هستن:
https://www.aparat.com/v/7y04v
https://www.aparat.com/v/7y04v
آپارات - سرویس اشتراک ویدیو
پایتون: صفر تا صد در ۲ ساعت!
CS50 2016 - Week 8 - Pythonٰٰدر این ویدیو خیلی سریع با پایتون آشنا میشین! همین طور مرور خیلی خوبی هست برای کسانی که قبل ترها دستشون به برنامه نویسی رفته و الان دنبال یه بهونه خوب برای شروع برنامه نویسی هستن.
🍄 Instead of arguing about whether results hold up, let’s push to provide enough information for others to repeat the experiments, says Philip Stark
https://www.nature.com/articles/d41586-018-05256-0
🔹 Science should be ‘show me’, not ‘trust me’; it should be ‘help me if you can’, not ‘catch me if you can’.
🔸 Just as I have pledged not to review papers that are not preproducible, I have also pledged not to submit papers without providing the software I used, and — to the extent permitted by law and ethics — the underlying data. I urge you to do the same. The commitment that Boyle made to the scientific community is even more crucial today.
https://www.nature.com/articles/d41586-018-05256-0
🔹 Science should be ‘show me’, not ‘trust me’; it should be ‘help me if you can’, not ‘catch me if you can’.
🔸 Just as I have pledged not to review papers that are not preproducible, I have also pledged not to submit papers without providing the software I used, and — to the extent permitted by law and ethics — the underlying data. I urge you to do the same. The commitment that Boyle made to the scientific community is even more crucial today.
Nature
Before reproducibility must come preproducibility
Instead of arguing about whether results hold up, let’s push to provide enough information for others to repeat the experiments, says Philip Stark.
🔹 PhD Fellowship in Tensor Networks/Hamiltonian Complexity
http://deepquantum.ai/phd-fellowship-in-tensor-networks-hamiltonian-complexity/
🤹♂ Experience in quantum theory/computing is not required. Students who have completed or are nearing completion of a masters degree with backgrounds in an area of mathematical sciences with interest in quantum computing are encouraged to apply.
http://deepquantum.ai/phd-fellowship-in-tensor-networks-hamiltonian-complexity/
🤹♂ Experience in quantum theory/computing is not required. Students who have completed or are nearing completion of a masters degree with backgrounds in an area of mathematical sciences with interest in quantum computing are encouraged to apply.
📈 MCMC sampling for dummies
http://twiecki.github.io/blog/2015/11/10/mcmc-sampling/
http://twiecki.github.io/blog/2015/11/10/mcmc-sampling/