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
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
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
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
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
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
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
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