Recurrent Shop
Framework for building complex recurrent neural networks with Keras

Recurrent shop providing a set of RNNCells, which can be added sequentially to a special layer called RecurrentContainer along with other layers such as Dropout and Activation, very similar to adding layers to a Sequential model in Keras. The RecurrentContainer then behaves like a standard Keras Recurrent instance. In case of RNN stacks, the computation is done depth-first, which results in significant speed ups.

https://github.com/datalogai/recurrentshop
#keras

Music auto-tagging models
and trained weights in keras/theano

How was it trained?
Using 29.1s music files in Million Song Dataset
split setting: A repo for split setting for an identical setting.
See https://arxiv.org/pdf/1609.04243v3.pdf

https://github.com/keunwoochoi/music-auto_tagging-keras
https://github.com/keunwoochoi/music-auto_tagging-keras/blob/master/slide-ismir-2016.pdf
#keras

SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
saliency-salgan-2017

https://imatge-upc.github.io/saliency-salgan-2017/
https://github.com/imatge-upc/saliency-salgan-2017
#Lasagne #theano #salience

YOLO9000 (детектирование 9000 категорий!)

We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detection method, both novel and drawn from prior work.

https://arxiv.org/pdf/1612.08242v1.pdf
https://www.reddit.com/r/MachineLearning/comments/5kr31v/r_yolo_9000/
http://pjreddie.com/yolo9000/

#yolo

Запись лекций с конференции
AINL FRUCT: Artificial Intelligence and Natural Language Conference
(Русские лекторы на англ. яз.)

DL Часть1 - https://www.lektorium.tv/lecture/29462#
DL Часть2 - https://www.lektorium.tv/lecture/29464
Все лекции https://www.lektorium.tv/conference/29503

Blockchains for Artificial Intelligence
from Decentralized Model Exchanges to Model Audit Trails

https://blog.bigchaindb.com/blockchains-for-artificial-intelligence-ec63b0284984#.homr8nm11

Нейронные сети, обученные технологией deep learning, создают собственный танец

C введением передовой технологии машинного обучения, deep learning, компьютеры начинают делать реальные вещи: учиться и создавать танцевальные движения, которые выглядят реалистично и повторить их способен практически любой человек.

В 5-часовой коллекции танцевальных данных нейронная сеть определила общий хореографический стиль движений, но дальнейшие движения сами по себе являются новой формой тех же движений. Это позволяет пользователям получить действительно уникальный контент.

http://neuronus.com/news-tech/1255-nejronnye-seti-obuchennye-tekhnologiej-deep-learning-sozdayut-sobstvennyj-tanets.html

How to do Object Detection with OpenCV [LIVE]

Data Science Bowl 2017
Новый конкурс на Kaggle c призом в 1M$.
Можете ли вы предсказать рак легких?

$1,000,000 in prizes awarded by the Arnold Foundation
120 credits to NVIDIA's Deep Learning Institute to the first 1,000 competitors
$500 in AWS credits from Amazon to the first 100 competitors

In the United States, lung cancer strikes 225,000 people every year, and accounts for $12 billion in health care costs. Early detection is critical to give patients the best chance at recovery and survival.

Two years ago, the office of the U.S. Vice President spearheaded a bold new initiative, the Cancer Moonshot, to make a decade's worth of progress in cancer prevention, diagnosis, and treatment in just 5 years.

In 2017, the Data Science Bowl will be a critical milestone in support of the Cancer Moonshot by convening the data science and medical communities to develop lung cancer detection algorithms.

https://www.kaggle.com/c/data-science-bowl-2017
http://www.datasciencebowl.com/