Онлайн курс "Программирование глубоких нейронных сетей на Python"
http://www.asozykin.ru/courses/nnpython
Материалы курса:
Введение.
Лекция “Искусственные нейронные сети”.
Лекция “Обучение нейронных сетей”.
Лекция “Библиотеки для глубокого обучения”.
Лекция “Распознавание рукописных цифр”.
Лекция “Анализ качества обучения нейронной сети”.
Практическая работа “Распознование рукописных цифр из набора данных MNIST на Keras”.
Лекция “Сверточные нейронные сети”.
Лекция “Распознавание объектов на изображениях”.
Практическая работа “Распознавание объектов на изображениях с помощью Keras”.
Рекуррентные нейронные сети.
Анализ текстов с помощью рекуррентных нейронных сетей.
https://www.youtube.com/watch?v=GX7qxV5nh5o&list=PLtPJ9lKvJ4oiz9aaL_xcZd-x0qd8G0VN_
http://www.asozykin.ru/courses/nnpython
Материалы курса:
Введение.
Лекция “Искусственные нейронные сети”.
Лекция “Обучение нейронных сетей”.
Лекция “Библиотеки для глубокого обучения”.
Лекция “Распознавание рукописных цифр”.
Лекция “Анализ качества обучения нейронной сети”.
Практическая работа “Распознование рукописных цифр из набора данных MNIST на Keras”.
Лекция “Сверточные нейронные сети”.
Лекция “Распознавание объектов на изображениях”.
Практическая работа “Распознавание объектов на изображениях с помощью Keras”.
Рекуррентные нейронные сети.
Анализ текстов с помощью рекуррентных нейронных сетей.
https://www.youtube.com/watch?v=GX7qxV5nh5o&list=PLtPJ9lKvJ4oiz9aaL_xcZd-x0qd8G0VN_
Тренируем нейронную сеть написанную на TensorFlow в облаке, с помощью Google Cloud ML и Cloud Shell
Конвертер моделей darknet на tensorflow.
Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++
https://github.com/thtrieu/darkflow
Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++
https://github.com/thtrieu/darkflow
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