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
http://web.stanford.edu/class/cs224n/reports.html
Датасет для экспериментов с One Shot Learning в NLP https://www.kaggle.com/generall/oneshotwikilinks
Датасет на основе WikiLinks, представляет собой скрауленные ссылки на статьи в Wikipedia, т.е. предполагается решать задачу Named Entity Linking.
Датасет на основе WikiLinks, представляет собой скрауленные ссылки на статьи в Wikipedia, т.е. предполагается решать задачу Named Entity Linking.
Kaggle
OneShot-wikilinks
One Shot Learning for Names Entity Linking
The Annotated Transformer: Line-by-Line PyTorch implementation of "Attention is All You Need"
http://nlp.seas.harvard.edu/2018/04/03/attention.html
http://nlp.seas.harvard.edu/2018/04/03/attention.html
Google Colaboratory: Automatic Tensorflow/Keras Checkpoints in your Google Drive
https://github.com/Zahlii/colab-tf-utils
https://github.com/Zahlii/colab-tf-utils
GitHub
GitHub - Zahlii/colab-tf-utils: Automatically backup keras/tensorflow models from Google's Colab service to your GoogleDrive based…
Automatically backup keras/tensorflow models from Google's Colab service to your GoogleDrive based on a keras callback! - GitHub - Zahlii/colab-tf-utils: Automatically backup keras/tensorfl...
Intro to XGBoost (detecting Parkinson's with XGBoost Classifiers)
https://medium.com/@priansh/detect-parkinsons-with-10-lines-of-code-intro-to-xgboost-51a4bf76b2e6
https://medium.com/@priansh/detect-parkinsons-with-10-lines-of-code-intro-to-xgboost-51a4bf76b2e6
Towards Data Science
Save Lives With 10 Lines of Code: Detecting Parkinson’s with XGBoost
Get started with XGBoost quickly and simply using the UCI Parkinson’s dataset and SKLearn.
Материалы курса "Глубинное обучение", прочитанного на ФКН ВШЭ весной 2018г.
https://github.com/aosokin/DL_CSHSE_spring2018
https://github.com/aosokin/DL_CSHSE_spring2018
GitHub
aosokin/dl_cshse_ami
Материалы курса "Глубинное обучение", ФКН ВШЭ, бакалаврская программа ПМИ - aosokin/dl_cshse_ami
Применение сверточных нейронных сетей для задач NLP
https://habrahabr.ru/company/ods/blog/353060/
https://habrahabr.ru/company/ods/blog/353060/
Хабр
Применение сверточных нейронных сетей для задач NLP
Когда мы слышим о сверточных нейронных сетях (CNN), мы обычно думаем о компьютерном зрении. CNN лежали в основе прорывов в классификации изображений — знаменитый...
A modular open-source image query dictionary app for Android using Tensorflow Lite (TFlite) and SQLite
https://github.com/queryChain/queryChain
https://github.com/queryChain/queryChain
GitHub
queryChain/queryChain
queryChain - An image query dictionary app used for identifying environmental and social certificates. This app is intended to be modular using Tensorflow Lite (Mobilenet_v1_1.0_224 model) with a ...
Keras Learning Rate Finder (+ SGDR and CLR)
https://github.com/nathanhubens/Learning-Rate
https://github.com/nathanhubens/Learning-Rate
GitHub
nathanhubens/Learning-Rate
Learning-Rate - Implementation of Learning Rate Finder, SGDR and Cyclical Learning Rate in Keras
How I monitor and track my machine learning experiments from anywhere (described in 13 tweets)
https://twitter.com/rememberlenny/status/983897644094447617
https://twitter.com/rememberlenny/status/983897644094447617
Twitter
Leonard Bogdonoff
Testing out @Cometml for training an image recognition model using Retina Net from the @PyImageSearch book https://t.co/WPH9sm8Rvj
Implementations of 15 NLP research papers using Keras, Tensorflow, and Scikit Learn.
https://github.com/GauravBh1010tt/DeepLearn
https://github.com/GauravBh1010tt/DeepLearn
GitHub
GitHub - GauravBh1010tt/DeepLearn: Implementation of research papers on Deep Learning+ NLP+ CV in Python using Keras, Tensorflow…
Implementation of research papers on Deep Learning+ NLP+ CV in Python using Keras, Tensorflow and Scikit Learn. - 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)
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)
Taiga Сorpus
Taiga is a corpus, where text sources and their meta-information are collected according to popular ML tasks.
An open-source corpus for machine learning.
Python module to easily generate text using a pretrained character-based recurrent neural network.
https://github.com/minimaxir/textgenrnn?reddit=1
https://github.com/minimaxir/textgenrnn?reddit=1
GitHub
minimaxir/textgenrnn
Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code. - minimaxir/textgenrnn
Which of the Hollywood stars is most similar to my voice?
https://github.com/andabi/voice-vector
https://github.com/andabi/voice-vector
GitHub
GitHub - andabi/voice-vector: Deep neural networks for getting text-independent speaker embedding written in TensorFlow
Deep neural networks for getting text-independent speaker embedding written in TensorFlow - GitHub - andabi/voice-vector: Deep neural networks for getting text-independent speaker embedding written...
Neural Style Transfer: A Review
https://github.com/ycjing/Neural-Style-Transfer-Papers
https://github.com/ycjing/Neural-Style-Transfer-Papers
GitHub
GitHub - ycjing/Neural-Style-Transfer-Papers: :pencil2: Neural Style Transfer: A Review
:pencil2: Neural Style Transfer: A Review. Contribute to ycjing/Neural-Style-Transfer-Papers development by creating an account on GitHub.
Задачи сегментации изображения с помощью нейронной сети Unet
http://blog.datalytica.ru/2018/03/unet.html
http://blog.datalytica.ru/2018/03/unet.html
blog.datalytica.ru
Задачи сегментации изображения с помощью нейронной сети Unet
Блог компании "Даталитика"
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
https://medium.com/nanonets/how-to-use-deep-learning-when-you-have-limited-data-part-2-data-augmentation-c26971dc8ced
Medium
Data Augmentation | How to use Deep Learning when you have Limited Data — Part 2
This article is a comprehensive review of Data Augmentation techniques for Deep Learning, specific to images. This is Part 2 of How to use…
Simple GUI app to collect mouse X,Y data for modelling or analysis
https://github.com/oist-cnru/mouse_drawing_app
https://github.com/oist-cnru/mouse_drawing_app
GitHub
oist-cnru/mouse_drawing_app
mouse_drawing_app - A simple GUI app for the generation of 2d mouse input data.