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Обзор материалов по машинному обучению (13 — 20 февраля 2017 года)

Представляю вашему вниманию подборку материалов по машинному обучению и анализу данных за прошедшую неделю, которые показались мне интересными.
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События предстоящей недели

1. image image Deep Learning: Now and Future of Robotics. Skolkovo-Skoltech-NVIDIA workshop [1]. 21 февраля.
2. image Data Science кейс-клуб [2]. 21 февраля.
3. image Data science завтрак [3]. 22 февраля.
4. image Тренировка по машинному обучению [4]. 25 февраля.

Учебные курсы, конференции

1. image Онлайн-курс «Введение в обработку естественного языка» с середины марта на stepik [5]. Страница прошлого года [6].
2. image Перезапуск курса «Neural Networks for Machine Learning» G.Hinton [7].
3. image Видео с конференции TensorFlow Developer Summit [8].
4. image Видео с конференции DataFest. Часть 1 [9]. Часть 2 [10]. Часть 3 [11]. Часть 4 [12].
5. image Семинар Practical Machine Learning от Яндекса [13] (видео). Тема: чат-боты. Запись ноябрьская, но попалась недавно.

6. image День открытых данных в Москве [14].
7. image Подборка материалов по ML и DM [15].
8. image The Best Intro to Data Science Courses — Class Central Career Guides [16].
9. image Опубликованы доклады ICLR 2017 [17], которая пройдет в апреле этого года во Франции.
10. image Oxford Deep NLP 2017 course [18].
UPD IliaSafonov [19]
11. image Конференция в Яндексе «Машинное обучение для бизнеса» [20].
12. image Kaggle запустил Google Cloud & YouTube-8M Video Understanding Challenge [21].
UPD jjdeluxe [22]
13. image Современные архитектуры диалоговых систем [23] — Анатолий Востряков, Segmento. Видео.

Новости

1. image Выходит TensorFlow 1.0 [24]
2. image Гугл выпустил дебаггер для TensorFlow tfdbg [25]
3. image Еженедельный дайджест от DataScienceCentral [26]
4. Еженедельный обзор портала HighScalability [27].

Научные статьи, практические реализации, датасеты

1. image Time Series Forecast Case Study with Python: Monthly Armed Robberies in Boston [28].
2. image Shopping datasets [29]. Belgium retail market dataset (donated by Tom Brijs): it contains the (anonymized) retail market basket data from an anonymous Belgian retail store.
3. image Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning [30].
4. image Нейронные сети для начинающих [31]. Часть 2.
5. image Spectral Clustering via Graph Filtering: Consistency on the High-Dimensional Stochastic Block Model [32].
6. image Performance of Distributed Deep Learning using ChainerMN [33].
7. image Model Mis-specification and Inverse Reinforcement Learning [34].
8. image PyTorch Implementation: seq2seq Translation [35].
9. image Automatically Segmenting Data With Clustering [36].
10. image Offline bilingual word vectors, orthogonal transformations and the inverted softmax [37].
11. image Parallel Long Short-Term Memory for Multi-stream Classification [38].
12. image Классификация [39] датасета, который недавно опубликовала Quora.
13. image Analyzing Six Deep Learning Tools for Music Generation — The Asimov Institute [40].
14. image HistWords: Word Embeddings for Historical Text [41].
15. image The Data Stack [42]. PDF, в котором собраны все инструменты для анализа данных.
16. image Data Coding 101 – Introduction to Bash [43].
17. image Time Series Forecast Case Study with Python: Annual Water Usage in Baltimore [44].
18. image Gaussian-Dirichlet Posterior Dominance in Sequential Learning [45].
19. image Реализация свёрточной нейронной сети [46] архитектуры InceptionV3 с использованием фреймворка Keras.
20. image Understanding Deep Learning Models in NLP [47].
21. image Web Scraping for Dataset Curation [48].
22. image Software Engineering vs Machine Learning Concepts [49].
23. image Frustratingly Short Attention Spans in Neural Language Modeling [50].
24. image Робот-собеседник на основе нейронной сети [51].
25. image Attacking machine learning with adversarial examples [52].
26. image Introduction to Anomaly Detection [53].
27. image 'AI brain scans' reveal what happens inside machine learning [54].
28. image Open Sourcing TensorFlowOnSpark: Distributed Deep Learning on Big-Data Clusters [55].
29. image Using NLP to understand how Twitter and the media reacted to the Super Bowl 51 ads battle [56].
30. image Reading Files — 3D Convolutional Neural Network [57]. Видео.
31. image Getting Started with Deep Learning [58].
32. image Time Series Forecast Study with Python: Monthly Sales of French Champagne [59].

Автор: Alf162

Источник [60]

Сайт-источник PVSM.RU: https://www.pvsm.ru

Путь до страницы источника: https://www.pvsm.ru/matematika/245089

Ссылки в тексте:

[1] Deep Learning: Now and Future of Robotics. Skolkovo-Skoltech-NVIDIA workshop: https://calendar.google.com/calendar/render?eid=M2NoMTRlcWNvdHE4bGpoaDE0NGYyZXJkdmcgb2xuOGowcTI0cm04aTVoMGEwYnVrYjVmdjBAZw&sf=true&output=xml#eventpage_6

[2] Data Science кейс-клуб: https://calendar.google.com/calendar/render?eid=Zmxwb2U4OTMxa2dhcTg1bnZhMmV2dTVnNWcgb2xuOGowcTI0cm04aTVoMGEwYnVrYjVmdjBAZw&sf=true&output=xml#eventpage_6

[3] Data science завтрак: https://calendar.google.com/calendar/render?eid=NmdwamlkMWc2aGltMmJiMmM4c2phYjlrNzBzajZiOW9jNWlqOGJiNTZ0aGplZGo1Y2dxM2NwMzVjY18yMDE3MDIyMlQwNjMwMDBaIG9sbjhqMHEyNHJtOGk1aDBhMGJ1a2I1ZnYwQGc&sf=true&output=xml#eventpage_6

[4] Тренировка по машинному обучению: https://calendar.google.com/calendar/render?eid=dGM0M2p0ZmFjdDMzdWYzMnVmZWgzZmEzOXMgb2xuOGowcTI0cm04aTVoMGEwYnVrYjVmdjBAZw&sf=true&output=xml#eventpage_6

[5] stepik: https://stepik.org

[6] прошлого года: http://kansas.ru/nlp2016/

[7] «Neural Networks for Machine Learning» G.Hinton: https://ru.coursera.org/learn/neural-networks

[8] TensorFlow Developer Summit: https://youtu.be/LqLyrl-agOw

[9] Часть 1: https://youtu.be/E62S4QNltLc

[10] Часть 2: https://youtu.be/fhZXqTGsunw

[11] Часть 3: https://youtu.be/KgEKFeg_BX8

[12] Часть 4: https://youtu.be/EEYTKgfiT-Q

[13] Семинар Practical Machine Learning от Яндекса: https://youtu.be/lAFuBy-1khc

[14] День открытых данных в Москве: https://habrahabr.ru/company/infoculture/blog/322100/

[15] Подборка материалов по ML и DM: https://yadi.sk/d/UG22a-9n3EDDsn

[16] The Best Intro to Data Science Courses — Class Central Career Guides: https://www.class-central.com/report/best-intro-data-science-courses/

[17] Опубликованы доклады ICLR 2017: https://openreview.net/group?id=ICLR.cc%2F2017%2Fconference&utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=revue

[18] Oxford Deep NLP 2017 course: https://github.com/oxford-cs-deepnlp-2017/lectures?utm_content=buffer5bf77&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

[19] IliaSafonov: https://habrahabr.ru/users/iliasafonov/

[20] «Машинное обучение для бизнеса» : https://events.yandex.ru/events/b-konf/16-feb-2017/

[21] Google Cloud & YouTube-8M Video Understanding Challenge: https://www.kaggle.com/c/youtube8m

[22] jjdeluxe: https://habrahabr.ru/users/jjdeluxe/

[23] Современные архитектуры диалоговых систем: https://www.youtube.com/watch?v=t6mXao2P4JU&feature=youtu.be

[24] Выходит TensorFlow 1.0: https://developers.googleblog.com/2017/02/announcing-tensorflow-10.html

[25] дебаггер для TensorFlow tfdbg: https://developers.googleblog.com/2017/02/debug-tensorflow-models-with-tfdbg.html?m=1

[26] Еженедельный дайджест от DataScienceCentral: http://www.datasciencecentral.com/profiles/blogs/weekly-digest-february-20

[27] Еженедельный обзор портала HighScalability: http://highscalability.com/blog/2017/2/19/stuff-the-internet-says-on-scalability-for-february-17th-201.html

[28] Time Series Forecast Case Study with Python: Monthly Armed Robberies in Boston: http://machinelearningmastery.com/time-series-forecast-case-study-python-monthly-armed-robberies-boston/

[29] Shopping datasets: http://recsyswiki.com/wiki/Grocery_shopping_datasets

[30] Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning: https://arxiv.org/pdf/1702.03274.pdf

[31] Нейронные сети для начинающих: https://habrahabr.ru/post/236757/

[32] Spectral Clustering via Graph Filtering: Consistency on the High-Dimensional Stochastic Block Model: https://arxiv.org/pdf/1702.03522.pdf

[33] Performance of Distributed Deep Learning using ChainerMN: http://chainer.org/general/2017/02/08/Performance-of-Distributed-Deep-Learning-Using-ChainerMN.html?utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=revue

[34] Model Mis-specification and Inverse Reinforcement Learning: https://jsteinhardt.wordpress.com/2017/02/07/model-mis-specification-and-inverse-reinforcement-learning/?utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=revue

[35] PyTorch Implementation: seq2seq Translation: https://github.com/spro/practical-pytorch/blob/master/seq2seq-translation/seq2seq-translation.ipynb?utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=revue

[36] Automatically Segmenting Data With Clustering: http://www.kdnuggets.com/2017/02/automatically-segmenting-data-clustering.html

[37] Offline bilingual word vectors, orthogonal transformations and the inverted softmax: https://arxiv.org/pdf/1702.03859.pdf

[38] Parallel Long Short-Term Memory for Multi-stream Classification: https://arxiv.org/pdf/1702.03402.pdf

[39] Классификация: https://explosion.ai/blog/quora-deep-text-pair-classification

[40] Analyzing Six Deep Learning Tools for Music Generation — The Asimov Institute: http://www.asimovinstitute.org/analyzing-deep-learning-tools-music/

[41] HistWords: Word Embeddings for Historical Text: http://nlp.stanford.edu/projects/histwords/

[42] The Data Stack: https://blog.liip.ch/archive/2017/02/13/data-stack.html

[43] Data Coding 101 – Introduction to Bash: https://data36.com/data-coding-101-introduction-bash/

[44] Time Series Forecast Case Study with Python: Annual Water Usage in Baltimore: http://machinelearningmastery.com/time-series-forecast-study-python-annual-water-usage-baltimore/

[45] Gaussian-Dirichlet Posterior Dominance in Sequential Learning: https://arxiv.org/pdf/1702.04126.pdf

[46] Реализация свёрточной нейронной сети: https://habr.ru/p/321834/

[47] Understanding Deep Learning Models in NLP: http://nlp.yvespeirsman.be/blog/understanding-deeplearning-models-nlp/

[48] Web Scraping for Dataset Curation: http://www.kdnuggets.com/2017/02/web-scraping-dataset-curation-part-2.html

[49] Software Engineering vs Machine Learning Concepts: http://www.machinedlearnings.com/2017/02/software-engineering-vs-machine.html

[50] Frustratingly Short Attention Spans in Neural Language Modeling: https://arxiv.org/pdf/1702.04521.pdf

[51] Робот-собеседник на основе нейронной сети: https://habrahabr.ru/post/321996/

[52] Attacking machine learning with adversarial examples: https://openai.com/blog/adversarial-example-research/

[53] Introduction to Anomaly Detection: https://www.datascience.com/blog/intro-to-anomaly-detection-learn-data-science-tutorials

[54] 'AI brain scans' reveal what happens inside machine learning: http://www.wired.co.uk/gallery/machine-learning-graphcore-pictures-inside-ai

[55] Open Sourcing TensorFlowOnSpark: Distributed Deep Learning on Big-Data Clusters: http://yahoohadoop.tumblr.com/post/157196317141/open-sourcing-tensorflowonspark-distributed-deep

[56] Using NLP to understand how Twitter and the media reacted to the Super Bowl 51 ads battle: http://blog.aylien.com/using-nlp-to-understand-how-twitter-and-the-media-reacted-to-the-super-bowl-51-ads-battle/

[57] Reading Files — 3D Convolutional Neural Network: https://www.youtube.com/watch?v=ulq9DjCJPDU&feature=youtu.be

[58] Getting Started with Deep Learning: http://www.svds.com/getting-started-deep-learning/

[59] Time Series Forecast Study with Python: Monthly Sales of French Champagne: http://machinelearningmastery.com/time-series-forecast-study-python-monthly-sales-french-champagne/

[60] Источник: https://habrahabr.ru/post/322188/?utm_source=habrahabr&utm_medium=rss&utm_campaign=best