💡 http://www.nature.com/news/the-new-thermodynamics-how-quantum-physics-is-bending-the-rules-1.22937

1️⃣ But what *is* a Neural Network? | Deep learning, Part 1
🔗 https://www.aparat.com/v/XkQFy

2️⃣ Gradient descent, how neural networks learn | Deep learning, part 2
🔗 https://www.aparat.com/v/uvUxW

3️⃣ What is backpropagation and what is it actually doing? | Deep learning
🔗 https://www.aparat.com/v/EZ9RV

3️⃣*️⃣ Backpropagation calculus | Appendix to deep learning
🔗 https://www.aparat.com/v/0tSKg

#سمینارهای_هفتگی گروه سیستم‌های پیچیده و علم شبکه دانشگاه شهید بهشتی

🔹دوشنبه، ۱۵ آبان ماه، ساعت ۴:۰۰ - کلاس۱ دانشکده فیزیک دانشگاه شهید بهشتی.

@carimi

#سمینار_عمومی این هفته
کوانتوم؛ مغز و هوش مصنوعی

-۳شنبه ۱۶ آبان؛ ساعت ۱۶
-تالار ابن هیثم، دانشکده فیزیک

کانال انجمن علمی دانشجویی فیزیک
@sbu_physics

♦️ https://pulitzercenter.org/reporting/can-math-be-used-predict-outbreak

🔖 Variational Inference: A Review for Statisticians

David M. Blei, Alp Kucukelbir, Jon D. McAuliffe

🔗 https://arxiv.org/pdf/1601.00670

📌 ABSTRACT
One of the core problems of modern statistics is to approximate difficult-to-compute probability densities. This problem is especially important in Bayesian statistics, which frames all inference about unknown quantities as a calculation involving the posterior density. In this paper, we review variational inference (VI), a method from machine learning that approximates probability densities through optimization. VI has been used in many applications and tends to be faster than classical methods, such as Markov chain Monte Carlo sampling. The idea behind VI is to first posit a family of densities and then to find the member of that family which is close to the target. Closeness is measured by Kullback-Leibler divergence. We review the ideas behind mean-field variational inference, discuss the special case of VI applied to exponential family models, present a full example with a Bayesian mixture of Gaussians, and derive a variant that uses stochastic optimization to scale up to massive data. We discuss modern research in VI and highlight important open problems. VI is powerful, but it is not yet well understood. Our hope in writing this paper is to catalyze statistical research on this class of algorithms

One of the best reviews on the computational nature of life: causality, adaptation and phase transitions 👇🏻👇👇🏼

Really helpful, thorough, and insightful network tutorial .ipynb

https://github.com/vtraag/4TU-CSS/blob/master/presentations/traag/notebook/Network.ipynb

👌🏾 An Introduction to Complex Systems: Society, Ecology, and Nonlinear Dynamics
http://physicstoday.scitation.org/doi/full/10.1063/PT.3.3766

Physics Today 70, 11, 51 (2017);https://doi.org/10.1063/PT.3.3766

⚙ PDF 👇🏼👇🏻👇🏽

🤠 https://plus.google.com/u/0/+johncbaez999/posts/SrQe3Bsd9kp

Using Big Data, Social Networks, and Agent-Based Modeling to Understand Information Diffusion
https://vimeo.com/241857550

⚙ This collection of over 500 MATLAB examples can help you with #machinelearning, #statistics, and #math problems
https://www.mathworks.com/examples/product-group/matlab-math-statistics-and-optimization?s_eid=PSM_da&hootPostID=70cb4d7118cfa4662cc041050f5e8ff1

⚡️ https://www.johndcook.com/blog/2017/11/08/why-is-kullback-leibler-divergence-not-a-distance/

🎞 Civilization Far From Equilibrium - Energy, Complexity, and Human Survival

https://www.perimeterinstitute.ca/videos/civilization-far-equilibrium-energy-complexity-and-human-survival

Abstract
Human societies use complexity – within their institutions and technologies – to address their various problems, and they need high-quality energy to create and sustain this complexity. But now greater complexity is producing diminishing returns in wellbeing, while the energetic cost of key sources of energy is rising fast. Simultaneously, humankind’s problems are becoming vastly harder, which requires societies to deliver yet more complexity and thus consume yet more energy. Resolving this paradox is the central challenge of the 21st century.
Thomas Homer-Dixon holds the CIGI Chair of Global Systems at the Balsillie School of International Affairs in Waterloo, Canada, and is a Professor at the University of Waterloo.