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Обзор материалов по машинному обучению № 3 (16 — 23 апреля 2018 года)

Добрый день! Это третий дайджест материалов по машинному обучению и анализу данных, который появился после длительного перерыва.

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События предстоящей недели

1. image Data science завтрак. 25 апреля с 9-30 до 12-00 в Кафе-пекарня «Райский Пирожок, pr-t. Mira, 26, стр. 1, Moskva [1]
2. image 5-й DataFest [2]. 28 апреля.
3. image NeuroHive 2018 [3]. Open source онлайн хакатон для разработчиков нейросетей.

Новости

1. image Врываемся в 2018 год с очередным большим релизом: выпуск версии 11.3 языка Wolfram Language и Mathematica [4].
2. image TensorFlow 1.8.0-rc0 [5].
3. image Еженедельный обзор портала HighScalability [6].
4. image Passing cs231n together (in Russian) [7].

Научные статьи, практические реализации, датасеты

1. image Python module to easily generate text using a pretrained character-based recurrent neural network. [8].
2. image Официальный резил тайга 2.0 [9].
3. image Swift for TensorFlow simulation [10].
4. image Text Classification with TensorFlow Estimators [11].
5. image Convolutional Neural Networks for Relation Extraction [12]
6. image It’s Training Cats and Dogs: NVIDIA Research Uses AI to Turn Cats into Dogs, Lions and Tigers, Too [13].
7. image Задачи сегментации изображения с помощью нейронной сети Unet [14].
8. image Which of the Hollywood stars is most similar to my voice? [15].
9. image Neural Style Transfer: A Review [16].
10. image Implementations of 15 NLP research papers using Keras, Tensorflow, and Scikit Learn. [17].
11. image The 2018 Stanford CS224n NLP course projects are now online. A lot of them are pretty impressive. [18].
12. image Collection of popular object detection models with pre-trained weights in TensorFlow [19].
13. image Representing Language with Recurrent and Convolutional Layers: An Authorship Attribution Example [20].
14. image Data Science Bowl 2018. Орисание решения победителя [21].
15. image Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking [22]
16. image Как делать отбор признаков и подбирать гиперпараметры с помощью проверки статистических гипотез [23].
17. image TwinGAN — Cross-Domain Translation of Human Portraits [24].
18. image Shared Autonomy via Deep Reinforcement Learning [25].
19. image Data Augmentation | How to use Deep Learning when you have Limited Data [26].
20. image The fall of RNN / LSTM [27].
21. image Еще одна статья о распознавании рабочих без касок нейросетями [28].
22. image Speed up TensorFlow Inference on GPUs with TensorRT [29].
23. image How Music Generated by Artificial Intelligence Is Reshaping — Not Destroying — The Industry [30].
24. image Semantic Segmentation — U-Net (Part 1) [31].
25. image Ассоциативные правила, или пиво с подгузниками [32].
26. image Understand how works Resnet… without talking about residual [33].
27. image SfSNet: Learning Shape, Reflectance and Illuminance of Faces ‘in the wild’ [34].
28. image A List Of Top 10 Deep Learning Papers, The 2018 Edition [35].
29. image Yann LeCun: Power and Limits of Deep Learning for Signal Understanding (ICASSP 2018 plenary) [36]. Видео.
30. image Simple Tensorflow implementation of „Multimodal Unsupervised Image-to-Image Translation“ [37].
31. image SNcGAN — Generate Conditional Images [38].
32. image Deploying Deep Learning Models on Kubernetes with GPUs [39].
33. image Применяем Deep Watershed Transform в соревновании Kaggle Data Science Bowl 2018 [40].
34. image How to implement a YOLO (v3) object detector from scratch in PyTorch: Part 1 [41], Part 2 [42], Part 3 [43], Part 4 [44], Part 5 [45].
35. image A Keras implementation of YOLOv3 (Tensorflow backend) inspired by allanzelener/YAD2K. [46].
36. image Pytorch implementation of MaxPoolingLoss. [47].
37. image An intuitive introduction to Generative Adversarial Networks (GANs) [48].
38. image Nice ideas about unit testing ML code [49].
39. image Что спрашивают на собеседовании по AI в Apple? [50].
40. image A collection of popular Data Science Competitions [51].
41. image Monte-Carlo Search for Magic: The Gathering [52].
42. image Датасет „Открытая семантика русского языка“ [53].

Предыдущий выпуск: Обзор материалов по машинному обучению [54].

Автор: Николай

Источник [55]

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

Путь до страницы источника: https://www.pvsm.ru/analiz-danny-h/278683

Ссылки в тексте:

[1] Кафе-пекарня «Райский Пирожок, pr-t. Mira, 26, стр. 1, Moskva: https://www.google.com/maps/place/%D0%A0%D0%B0%D0%B9%D1%81%D0%BA%D0%B8%D0%B9+%D0%9F%D0%B8%D1%80%D0%BE%D0%B6%D0%BE%D0%BA/@55.777967,37.6309524,17z/data=!3m1!4b1!4m5!3m4!1s0x46b54a723ac22ea9:0xfe1387c8a89c2731!8m2!3d55.777967!4d37.6331411

[2] 5-й DataFest: http://www.datafest.ru/

[3] NeuroHive 2018 : http://neurohive.io/

[4] Врываемся в 2018 год с очередным большим релизом: выпуск версии 11.3 языка Wolfram Language и Mathematica: https://habrahabr.ru/company/wolfram/blog/353972/

[5] TensorFlow 1.8.0-rc0: https://github.com/tensorflow/tensorflow/releases/tag/v1.8.0-rc0

[6] Еженедельный обзор портала HighScalability: http://highscalability.com/blog/2018/4/20/stuff-the-internet-says-on-scalability-for-april-20th-2018.html

[7] Passing cs231n together (in Russian): https://github.com/Yorko/mlcourse_open/wiki/Passing-cs231n-together-(in-Russian)

[8] Python module to easily generate text using a pretrained character-based recurrent neural network.: https://github.com/minimaxir/textgenrnn?reddit=1

[9] Официальный резил тайга 2.0: https://tatianashavrina.github.io/taiga_site/

[10] Swift for TensorFlow simulation: https://heartbeat.fritz.ai/swift-for-tensorflow-simulation-34e39ccda83f?gi=aee37b5b32c2

[11] Text Classification with TensorFlow Estimators: http://ruder.io/text-classification-tensorflow-estimators/

[12] Convolutional Neural Networks for Relation Extraction: https://github.com/roomylee/cnn-relation-extraction

[13] It’s Training Cats and Dogs: NVIDIA Research Uses AI to Turn Cats into Dogs, Lions and Tigers, Too: https://blogs.nvidia.com/blog/2018/04/15/nvidia-research-image-translation/

[14] Задачи сегментации изображения с помощью нейронной сети Unet: http://blog.datalytica.ru/2018/03/unet.html

[15] Which of the Hollywood stars is most similar to my voice?: https://github.com/andabi/voice-vector

[16] Neural Style Transfer: A Review: https://github.com/ycjing/Neural-Style-Transfer-Papers

[17] Implementations of 15 NLP research papers using Keras, Tensorflow, and Scikit Learn.: https://github.com/GauravBh1010tt/DeepLearn

[18] The 2018 Stanford CS224n NLP course projects are now online. A lot of them are pretty impressive.: http://web.stanford.edu/class/cs224n/reports.html

[19] Collection of popular object detection models with pre-trained weights in TensorFlow: https://github.com/taehoonlee/tensornets/releases/tag/0.3.1

[20] Representing Language with Recurrent and Convolutional Layers: An Authorship Attribution Example: https://hergott.github.io/language-representation-rnn-cnn/

[21] Data Science Bowl 2018. Орисание решения победителя: https://www.kaggle.com/c/data-science-bowl-2018/discussion/54741

[22] Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking: https://github.com/filipradenovic/revisitop

[23] Как делать отбор признаков и подбирать гиперпараметры с помощью проверки статистических гипотез: https://medium.com/@vadim_uvarov/feature-selection-using-statistical-testing-2d8e7b5e27b8

[24] TwinGAN — Cross-Domain Translation of Human Portraits: https://github.com/jerryli27/TwinGAN

[25] Shared Autonomy via Deep Reinforcement Learning: http://bair.berkeley.edu/blog/2018/04/18/shared-autonomy/

[26] Data Augmentation | How to use Deep Learning when you have Limited Data: https://medium.com/nanonets/how-to-use-deep-learning-when-you-have-limited-data-part-2-data-augmentation-c26971dc8ced

[27] The fall of RNN / LSTM: https://towardsdatascience.com/the-fall-of-rnn-lstm-2d1594c74ce0

[28] Еще одна статья о распознавании рабочих без касок нейросетями: https://habrahabr.ru/post/354092/

[29] Speed up TensorFlow Inference on GPUs with TensorRT: https://medium.com/tensorflow/speed-up-tensorflow-inference-on-gpus-with-tensorrt-13b49f3db3fa

[30] How Music Generated by Artificial Intelligence Is Reshaping — Not Destroying — The Industry: https://www.billboard.com/articles/business/8333911/artificial-intelligence-music-reshaping-destroying-industry

[31] Semantic Segmentation — U-Net (Part 1): https://medium.com/@keremturgutlu/semantic-segmentation-u-net-part-1-d8d6f6005066

[32] Ассоциативные правила, или пиво с подгузниками: https://habrahabr.ru/company/ods/blog/353502/

[33] Understand how works Resnet… without talking about residual: https://medium.com/@pierre_guillou/understand-how-works-resnet-without-talking-about-residual-64698f157e0c

[34] SfSNet: Learning Shape, Reflectance and Illuminance of Faces ‘in the wild’: https://senguptaumd.github.io/SfSNet/

[35] A List Of Top 10 Deep Learning Papers, The 2018 Edition: https://www.techleer.com/articles/517-a-list-of-top-10-deep-learning-papers-the-2018-edition/

[36] Yann LeCun: Power and Limits of Deep Learning for Signal Understanding (ICASSP 2018 plenary): https://youtu.be/7R0NH3Szj-s

[37] Simple Tensorflow implementation of „Multimodal Unsupervised Image-to-Image Translation“: https://github.com/taki0112/MUNIT-Tensorflow

[38] SNcGAN — Generate Conditional Images: http://adeel.io/sncgan/

[39] Deploying Deep Learning Models on Kubernetes with GPUs: https://blogs.technet.microsoft.com/machinelearning/2018/04/19/deploying-deep-learning-models-on-kubernetes-with-gpus/

[40] Применяем Deep Watershed Transform в соревновании Kaggle Data Science Bowl 2018: https://habrahabr.ru/post/354040/

[41] Part 1: https://blog.paperspace.com/how-to-implement-a-yolo-object-detector-in-pytorch/

[42] Part 2: https://blog.paperspace.com/how-to-implement-a-yolo-v3-object-detector-from-scratch-in-pytorch-part-2/

[43] Part 3: https://blog.paperspace.com/how-to-implement-a-yolo-v3-object-detector-from-scratch-in-pytorch-part-3/

[44] Part 4: https://blog.paperspace.com/how-to-implement-a-yolo-v3-object-detector-from-scratch-in-pytorch-part-4/

[45] Part 5: https://blog.paperspace.com/how-to-implement-a-yolo-v3-object-detector-from-scratch-in-pytorch-part-5/

[46] A Keras implementation of YOLOv3 (Tensorflow backend) inspired by allanzelener/YAD2K.: https://github.com/qqwweee/keras-yolo3

[47] Pytorch implementation of MaxPoolingLoss.: https://github.com/BelBES/mpl.pytorch

[48] An intuitive introduction to Generative Adversarial Networks (GANs): https://medium.freecodecamp.org/an-intuitive-introduction-to-generative-adversarial-networks-gans-7a2264a81394

[49] Nice ideas about unit testing ML code: https://medium.com/@keeper6928/how-to-unit-test-machine-learning-code-57cf6fd81765

[50] Что спрашивают на собеседовании по AI в Apple?: https://medium.com/acing-ai/apple-ai-interview-questions-acing-the-ai-interview-803a65b0e795

[51] A collection of popular Data Science Competitions: https://github.com/iphysresearch/DataSciComp#active-competitons-to-join

[52] Monte-Carlo Search for Magic: The Gathering: https://hackernoon.com/monte-carlo-search-for-magic-the-gathering-6ca60750fcc6

[53] Датасет „Открытая семантика русского языка“: https://github.com/dkulagin/kartaslov/tree/master/dataset/open_semantics

[54] Обзор материалов по машинному обучению: https://habrahabr.ru/post/322660/

[55] Источник: https://habr.com/post/354124/?utm_source=habrahabr&utm_medium=rss&utm_campaign=354124