​Reproducibility tool for #Jupyter Notebooks

Link: https://mybinder.org

#DS #github #reproducibleresearch

🔗 Binder (beta)

​POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and their Solutions through the Paired Open-Ended Trailblazer

POET: it generates its own increasingly complex, diverse training environments & solves them. It automatically creates a learning curricula & training data, & potentially innovates endlessly.

Link: https://eng.uber.com/poet-open-ended-deep-learning/

#RL #Uber

🔗 POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and their Solutions through the Paired Open-Ended Trailblazer
Uber AI Labs introduces the Paired Open-Ended Trailblazer (POET), an algorithm that leverages open-endedness to push the bounds of ML.

​Scikit-learn drops support of Python2.7 with new PR.

It means scikit-learn master now requires Python >= 3.5.

https://github.com/scikit-learn/scikit-learn/pull/12639

#scikitlearn

🔗 MRG Drop legacy python / remove six dependencies by amueller · Pull Request #12639 · scikit-learn/sc
Tries to drop legacy python (2.7) and remove six everywhere.

​​​Super-resolution GANs for improving the texture resolution of old games.

It is what it is. #GAN to enhance textures in old games making them look better.

ArXiV: https://arxiv.org/abs/1809.00219
Link: https://www.gamespot.com/forums/pc-mac-linux-society-1000004/esrgan-is-pretty-damn-amazing-trying-max-payne-wit-33449670/

#gaming #superresolution

🔗 ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts. To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block (RRDB) without batch normalization as the basic network building unit. Moreover, we borrow the idea from relativistic GAN to let the discriminator predict relative realness instead of the absolute value. Finally, we improve the perceptual loss by using the features before activation, which could provide stronger supervision for brightness consistency and texture recovery. Benefiting from these improvements, the proposed ESRGAN achieves consistently better visual quality with more realistic an

🎥 TMPA School 2018 Saratov: Компьютерная обработка текстов (часть 3, практика)
👁 1 раз ⏳ 1485 сек.
Стажер-исследователь: Факультет компьютерных наук / Департамент больших данных и информационного поиска / Научно-учебная лаборатория моделирования и управления сложными системами, НИУ ВШЭ

Запись с экрана по ссылке: https://yadi.sk/i/_e7BThoZY20-zA

TMPA School 2018
Тестирование программного обеспечения, анализ данных и машинное обучение
https://school.tmpaconf.org/

🎥 TMPA School 2018 Saratov: Компьютерная обработка текстов (часть 2, практика)
👁 1 раз ⏳ 2822 сек.
Стажер-исследователь: Факультет компьютерных наук / Департамент больших данных и информационного поиска / Научно-учебная лаборатория моделирования и управления сложными системами, НИУ ВШЭ

Доп. материалы: https://yadi.sk/d/aqqA84nKI36X5w

TMPA School 2018
Тестирование программного обеспечения, анализ данных и машинное обучение
https://school.tmpaconf.org/

🎥 Amazon AI Conclave 2018 - The Alexa Fund - India outlook by Rodrigo Prudencio
👁 1 раз ⏳ 667 сек.
Check out more details about Amazon AI Conclave at - https://amzn.to/2Qv2PaI.
The Alexa Fund - India outlook session by Rodrigo Prudencio, Amazon Corporate Development. Amazon AI Conclave is a free event for business leaders, data scientists, engineers and developers to learn about Amazon's machine learning services, and real-world use cases developed by our customers. This program helps you understand how to build smart, customer-centric, scalable solutions in the cloud and on the edge using Amazon AI, AWS

Problem Solving With SQL
https://towardsdatascience.com/problem-solving-with-sql-b0ad58fe8643?source=collection_home---4------2---------------------

​Explaining Complex Machine Learning Models With LIME - Shirin Glander (codecentric AG)

🔗 Explaining Complex Machine Learning Models With LIME - Shirin Glander (codecentric AG)
Dr. Shirin Glander lives in Münster and works as a Data Scientist at codecentric AG, she has lots of practical experience. Traditional machine learning workf...

https://habr.com/post/435648/

Бот генерирует учебники из статей Википедии

#machinelearning #neuralnets #deeplearning #машинноеобучение

Наш телеграмм канал - https://t.me/ai_machinelearning_big_data

TMPA School 2018 Saratov: Компьютерная обработка текстов (часть 1)
https://www.youtube.com/watch?v=cFGebptla2g

🎥 TMPA School 2018 Saratov: Компьютерная обработка текстов (часть 1)
👁 1 раз ⏳ 2516 сек.
Стажер-исследователь: Факультет компьютерных наук / Департамент больших данных и информационного поиска / Научно-учебная лаборатория моделирования и управления сложными системами, НИУ ВШЭ

Смотреть презентацию: https://speakerdeck.com/exactpro/komp-iutiernaia-obrabotka-tiekstov-5bc65162-5dfe-4765-b0a7-5ca03ca7b94b

Запись экрана: https://yadi.sk/i/7ZwPlhSUteHB8w

TMPA School 2018
Тестирование программного обеспечения, анализ данных и машинное обучение
https://school.tmpaconf.org/

Apache Ignite — распределенный Machine Learning с Java API

🎥 Apache Ignite — распределенный Machine Learning с Java API
👁 1 раз ⏳ 2391 сек.
Юрий Бабак, Apache Ignite Committer на митапе в СПб 26.12.2018
https://www.meetup.com/ru-RU/St-Petersburg-Apache-Ignite-Meetup/events/257128451/

​«Этот анализ не покрывает все доступные данные, но показывает те из них, которые показались мне наиболее интересными. Желающие могут провести своё исследование на этих данных».

Анализируем результаты 2018 Kaggle ML & DS Survey: http://amp.gs/E6mf

🔗 Анализ результатов 2018 Kaggle ML & DS Survey
Kaggle — известная платформа для проведения соревнований по машинному обучению на которой количество зарегистрированных пользователей перевалило за 2.5...

​Как учиться с помощью машинного обучения у экспертов в Dota 2

В предыдущей статье от Питерской Вышки мы показывали, как при помощи машинного обучения можно искать баги в программном коде. В этом посте расскажем о том, как мы вместе с JetBrains Research пытаемся использовать один из самых интересных, современных и быстроразвивающихся разделов машинного обучения — обучение с подкреплением — как в реальных практических задачах, так и на модельных примерах.

https://habr.com/company/hsespb/blog/435636/

🔗 Как учиться с помощью машинного обучения у экспертов в Dota 2
В предыдущей статье от Питерской Вышки мы показывали, как при помощи машинного обучения можно искать баги в программном коде. В этом посте расскажем о том, как...

This AI Learns From Humans…and Exceeds Them

🎥 This AI Learns From Humans…and Exceeds Them
👁 1 раз ⏳ 255 сек.
The paper "Reward learning from human preferences and demonstrations in Atari" is available here:
https://arxiv.org/abs/1811.06521

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We would like

​instance weighted CE loss for PyTorch

https://gist.github.com/nasimrahaman/a5fb23f096d7b0c3880e1622938d0901

🔗 Pytorch instance-wise weighted cross-entropy loss
Pytorch instance-wise weighted cross-entropy loss. GitHub Gist: instantly share code, notes, and snippets.

​Deep Learning course: lecture slides and lab notebooks from Master Datascience Paris Saclay with a focus on applications

https://m2dsupsdlclass.github.io/lectures-labs/?fbclid=IwAR3V4f2UB9U9lwq9H_Vzr_E6gfUJJ_tDuXnsw0_459hCRVIjOAsm8B_Hd64

🔗 Deep Learning course: lecture slides and lab notebooks
Slides and Jupyter notebooks for the Deep Learning lectures at M2 Data Science Université Paris Saclay

​A visual exploration of Gaussian Processes: beautiful interactive plots and a brief tutorial to make GPs more approachable

Link: https://www.jgoertler.com/visual-exploration-gaussian-processes/

#Statistics #GP #GaussianProcesses

🔗 A Visual Exploration of Gaussian Processes

Deep Learning Basics: Introduction and Overview - MIT

🎥 Deep Learning Basics: Introduction and Overview - MIT
👁 1 раз ⏳ 4087 сек.
An introductory lecture overviewing the basics of deep learning including a few key ideas, subfields, and the big picture of why neural networks have inspired and energized an entire new generation of researchers. For more lecture videos visit our website or follow code tutorials on our GitHub repo.

INFO:
Website: https://deeplearning.mit.edu
GitHub: https://github.com/lexfridman/mit-deep-learning
Slides: http://bit.ly/deep-learning-basics-slides
Playlist: http://bit.ly/deep-learning-playlist

OUTLINE:
0:0

​Obtaining Calibrated Probabilities from Machine Learning Models

🔗 Obtaining Calibrated Probabilities from Machine Learning Models
A predictive model is well-calibrated if the probabilities that it predicts match the probability that it is correct. This lecture will summarize our current...