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

Официальный резил тайга 2.0. Краткая выжимка, по ссылке более детально:
We have gathered the resources with respect to popular NLP-problems:

thematic modelling - news with theme tags, all the sites which provide rubrication (news, poems, prose)
readability of texts - a popular science magazine NPlus1 has a readability metric for each text, provided by editor.
NER and fact extraction - news with references to mentioned person’s page or wiki-information, news with personalia tags
key-words extraction - news with key-word tags, hashtags on social media
authorship attribution - all the texts with author information - magazines, news, and more important - social media - with gender, age, city, time and education mark-up.
chat-bot training - open-source film subnoscripts
text generation - any resource depending on genre
rare words studying, frequency dictionaries - literary magazines, social media
morphological and syntactic parsers - any resource with respect to the genre
Taiga corpus is an ambitious project to become the largest fully available webcorpus constructed from open text sources. Taiga corpus is:

open source, CC BY-SA 3.0
big - about 5 billion words by now
sorted by datasets applicable to different machine laearning tasks
made by linguists, experienced in text crawling, parsing and filtering
rich with metainformation
POS-tagged and syntactically tagged in Universal Dependencies
https://tatianashavrina.github.io/taiga_site/

Создатели:
Tatiana Shavrina (rybolos@gmail.com)
Yana Kurmachova (yana.kurmacheva@gmail.com)

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

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

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

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

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

Simple GUI app to collect mouse X,Y data for modelling or analysis
https://github.com/oist-cnru/mouse_drawing_app

Reproducing UberAI's "Deep Neuroevolution" paper
https://towardsdatascience.com/paper-repro-deep-neuroevolution-756871e00a66

Full-stack machine learning solutions | Hive
https://thehive.ai/blog/simple-ml-serving

Обзор материалов по машинному обучению № 3 (16 — 23 апреля 2018 года)
https://habrahabr.ru/post/354124/

Как мы участвовали в хакатоне от OpenData
https://habrahabr.ru/company/spbau/blog/354150/

Learning London Dating Profiles
https://medium.com/@LeonFedden/learning-london-dating-profiles-8f13403af1d9

Применение рекуррентных слоев для решения многоходовок
https://habr.com/post/354220/

pytorch implementation of Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/atulkum/pointer_summarizer

Announcing PyTorch 1.0 for both research and production
https://code.facebook.com/posts/172423326753505/

Glow: Compiler for Neural Network hardware accelerators
https://github.com/pytorch/glow/

Comparing Sentence Similarity Methods
http://nlp.town/blog/sentence-similarity/

Identifying Natural Language with 99% accuracy using Machine Learning (Python and Scikit-Learn)
https://youtu.be/5Bc6-uDcnqg

torchplus — implements the + operator on pytorch layers, returning nn.Sequential
https://github.com/knighton/torchplus

Fountain - Natural Language Data Augmentation Tool
https://github.com/tzano/fountain