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General Activity For Now.md

@@ -12,6 +12,11 @@
 * Future Learning 
 * [HSE Course (GitHub)](https://github.com/esokolov/ml-course-hse/)
 * [Microsoft Professional Program for Artificial Intelligence](https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/)
+* [Blommberg ML](https://bloomberg.github.io/foml/#home)
+* [Toronto DL](http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/)
+* [Elements of AI](https://course.elementsofai.com)
+* [Julia Scientific Programming Coursera](https://ru.coursera.org/learn/julia-programming)
+* [Stepic Julia](https://stepik.org/course/2407)
 
 
 ## Stepic Additional
@@ -24,9 +29,14 @@
 * ODS ML Course Open
 * Deep NLP MIPT
 * cs224n (NLP) 
+* cs231n (DeepLearning)
+* [Carnegie Melon Deep Learning Course](http://deeplearning.cs.cmu.edu) 
 
 ## Курсы, что я уже прошел
 
+## Летняя школа, что я помогал проводить
+[Летняя Школа. Мастерская Deep Learning](http://letnyayashkola.org/deeplearning/)
+
 -----
 
 
@@ -53,7 +63,21 @@
 * [Ассоциативные правила, или пиво с подгузниками](https://habrahabr.ru/company/ods/blog/353502/)
 * [Connection between absract algebra and high school algebra](https://blogs.ams.org/matheducation/2015/12/10/connections-between-abstract-algebra-and-high-school-algebra-a-few-connections-worth-exploring/)
 * [Instance Embedding: Segmentation Without Proposals](https://medium.com/@barvinograd1/instance-embedding-instance-segmentation-without-proposals-31946a7c53e1)
-
+* [Обзор топологий глубоких сверточных сетей](https://habrahabr.ru/company/mailru/blog/311706/)
+* [Generative Adversarial Nets and Variational Autoencoders at ICML 2018](https://medium.com/peltarion/generative-adversarial-nets-and-variational-autoencoders-at-icml-2018-6878416ebf22)
+* [Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification](https://www.nature.com/articles/s41598-018-30619-y)
+* [CERN Project Sees Orders-of-Magnitude Speedup with AI Approach](https://www.hpcwire.com/2018/08/14/cern-incorporates-ai-into-physics-based-simulations/)
+* [***Large-Scale Study of Curiosity-Driven Learning.***](https://pathak22.github.io/large-scale-curiosity/)
+* [Building a text classification model with TensorFlow Hub and Estimators](https://medium.com/tensorflow/building-a-text-classification-model-with-tensorflow-hub-and-estimators-3169e7aa568)
+* [Moving Beyond Translation with the Universal Transformer.](https://ai.googleblog.com/2018/08/moving-beyond-translation-with.html)
+* [Explaining Black-Box Machine Learning Models - Code Part 1: tabular data + caret + iml](https://shirinsplayground.netlify.com/2018/07/explaining_ml_models_code_caret_iml/)
+* [Keras DNN Part 2](https://shirinsplayground.netlify.com/2018/06/keras_fruits_lime/)
+* [Boosting Part 3](https://shirinsplayground.netlify.com/2018/07/explaining_ml_models_code_text_lime/)
+* [Recent Advances for a Better Understanding of Deep Learning − Part I](https://towardsdatascience.com/recent-advances-for-a-better-understanding-of-deep-learning-part-i-5ce34d1cc914)
+* [What is a Generative Adversarial Network? ](http://hunterheidenreich.com/blog/what-is-a-gan/)
+
+
+* [Think Julia: How to Think Like a Computer Scientist](https://benlauwens.github.io/ThinkJulia.jl/latest/book.html)
 
 ## Штуки, что я прочитал
 
@@ -63,12 +87,33 @@
 * [Blockchain]()
 * [Ehtereum](https://vas3k.ru/blog/ethereum/)
 * [Автоэнкодеры в Keras](https://habrahabr.ru/post/331382/)
+* [Разброс и смещение Дяконова](https://alexanderdyakonov.wordpress.com/2018/04/25/%D1%81%D0%BC%D0%B5%D1%89%D0%B5%D0%BD%D0%B8%D0%B5-bias-%D0%B8-%D1%80%D0%B0%D0%B7%D0%B1%D1%80%D0%BE%D1%81-variance-%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D0%B8-%D0%B0%D0%BB%D0%B3%D0%BE%D1%80%D0%B8%D1%82/)
+* [Распонзнавание сцен и достопримечательностей](https://habr.com/company/jugru/blog/419501/)
+* [Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples](https://blog.acolyer.org/2018/08/15/obfuscated-gradients-give-a-false-sense-of-security-circumventing-defenses-to-adversarial-examples/)
+* [When DNNs go wrong – adversarial examples and what we can learn from them](https://blog.acolyer.org/2017/02/28/when-dnns-go-wrong-adversarial-examples-and-what-we-can-learn-from-them/)
+* [Understanding, generalisation, and transfer learning in deep neural networks](https://blog.acolyer.org/2017/02/27/understanding-generalisation-and-transfer-learning-in-deep-neural-networks/)
+* [Universal adversarial perturbations](https://blog.acolyer.org/2017/09/12/universal-adversarial-perturbations/)
+* [Delayed impact of fair machine learning](https://blog.acolyer.org/2018/08/13/delayed-impact-of-fair-machine-learning/)
+* [Почему хватит считать нейронные сети черным ящиком?](https://habr.com/post/420381/)
+* [Ultimate guide to handle Big Datasets for Machine Learning using Dask (in Python)](https://www.analyticsvidhya.com/blog/2018/08/dask-big-datasets-machine_learning-python/)
+* [OpenCV People Counter](https://www.pyimagesearch.com/2018/08/13/opencv-people-counter/)
+* [Ложь, наглая ложь и причинный вывод (causal inference)](https://ailev.livejournal.com/1435703.html)
+* [pandas on ray early lessons](https://rise.cs.berkeley.edu/blog/pandas-on-ray-early-lessons/)
+* [Red Flags In DS interview](http://hookedondata.org/Red-Flags-in-Data-Science-Interviews/)
+* [Classifying physical activity from smartphone data (Keras and R)](http://blogs.rstudio.com/tensorflow/posts/2018-07-17-activity-detection/)
+* [Keras for R](http://blogs.rstudio.com/tensorflow/posts/2017-09-06-keras-for-r/)
+* [Simple audio classification in keras in R](http://blogs.rstudio.com/tensorflow/posts/2018-06-06-simple-audio-classification-keras/) 
+* [Пицца аля-semi-supervised](https://habr.com/company/ods/blog/422873/)
+* [Определение цвета автомобилей с использованием нейронных сетей и TensorFlow](https://habr.com/company/intel/blog/422689/)
+* 
 * 
 -----
 
 ## Штуки, что я написал, перевел
 
-* 
+* [Применяем Deep Watershed Transform в соревновании Kaggle Data Science Bowl 2018](https://habrahabr.ru/post/354040/)
+* [Из спутниковых снимков в графы (cоревнование SpaceNet Road Detector) — попадание топ-10 и код ](https://habrahabr.ru/post/349068/)
+* [Соревнование Pri-matrix Factorization на DrivenData с 1ТБ данных — как мы заняли 3 место](https://habrahabr.ru/post/348540/)
 
 
 # Видео
@@ -85,6 +130,7 @@
 * [Manning And Le Cun talks about Innate Prior Chomsky](https://youtu.be/fKk9KhGRBdI)
 * [Attention is all you need by Ilya Polosuhin](https://www.youtube.com/watch?v=I0nX4HDmXKc)
 * [Simon says LSTM](https://www.youtube.com/watch?v=wYI7RZz4Rz0)
+* [Интервью с Виктором Рогуленко](http://youtube.com/watch?v=ymSqI0hVj-Q)
 
 ## Видео, что я посмотрел 
 
@@ -93,4 +139,20 @@
 * [Keras init bias](https://www.youtube.com/watch?v=zralyi2Ft20)
 * [Генератор текста цепями маркова](https://tproger.ru/translations/markov-chains/)
 * [Ethereum work like](https://www.youtube.com/watch?v=a-Azm3nEuUI)
+* [Dstl Safe Passage: детекция и классификация траспортных средств — Владимир Игловиков](https://www.youtube.com/watch?v=NV9LSUIVkWA&feature=youtu.be&t=1247)
+* [Анализ больших данных в физике элементарных частиц](https://www.youtube.com/watch?v=SgI8S8ltBKc&feature=youtu.be)
+* [Large-Scale Study of Curiosity-Driven Learning](https://youtu.be/l1FqtAHfJLI)
+* [Подтипирование в Julia: рациональная реконструкция](https://www.youtube.com/watch?v=nnOJfPIrFdM)
+* [Semantic Folding: a new model for intelligent text processing](https://www.youtube.com/watch?v=HLuRQKzYbb8&feature=youtu.be)
+* [Применение карты Кохонена для классификации](https://www.youtube.com/watch?v=5FiH88Rs8Hc)
+* [Lambda Calculus](https://youtu.be/eis11j_iGMs)
+* [Essentials: Functional Programming's Y Combinator](https://www.youtube.com/watch?v=9T8A89jgeTI)
+* [Illustrated Guide to Recurrent Neural Networks](https://youtu.be/LHXXI4-IEns)
+* [illustrated guide to LSTM's and GRU's](https://www.youtube.com/watch?v=8HyCNIVRbSU)
+* [Visual Rhythm Beat](https://www.youtube.com/watch?v=K3z68mOLbNo&feature=youtu.be)
+
+
+
+## Работы, что я прочитал
 
+*