In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.
В этом задании вам предстоит проверить работу центральной предельной теоремы, а также поработать с генерацией случайных чисел и построением графиков в Питоне.
- Выберите ваше любимое непрерывное распределение (чем меньше оно будет похоже на нормальное, тем интереснее; попробуйте выбрать какое-нибудь распределение из тех, что мы не обсуждали в курсе).
- Прочитать, что такое непрерывное распределение, выбрать то, которое нравится больше всех.
- Кратко описать его в своём ноутбуке (определение + формулы + график + почему нравится).
- Сгенерируйте из него выборку объёма 1000, постройте гистограмму выборки и нарисуйте поверх неё теоретическую плотность распределения вашей случайной величи
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##Sequence to Sequence -- Video to Text
Paper : ICCV 2015 PDF
Download Model: S2VT_VGG_RGB_MODEL (333MB)
#!/usr/bin/env python | |
# | |
# Shows GOP structure of video file. Useful for checking suitability for HLS and DASH packaging. | |
# Example: | |
# | |
# $ iframe-probe.py myvideo.mp4 | |
# GOP: IPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP 60 CLOSED | |
# GOP: IPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP 60 CLOSED | |
# GOP: IPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP 60 CLOSED | |
# GOP: IPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP 60 CLOSED |
##VGG19 model for Keras
This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns
Convolutional neural networks for emotion classification from facial images as described in the following work:
Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns, Proc. ACM International Conference on Multimodal Interaction (ICMI), Seattle, Nov. 2015
Project page: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/
If you find our models useful, please add suitable reference to our paper in your work.
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Ask Your Neurons: A Neural-Based Approach to Answering Questions About Images
- Mateusz Malinowski, Marcus Rohrbach, Mario Fritz
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Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books
- Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler
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Learning Query and Image Similarities With Ranking Canonical Correlation Analysis
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Wah Ngo
- completely decentralized architecture ;
- a content-addressed file system (through cryptographic-hash) ;
- combines Kademlia + BitTorrent + Git
- Can I delete my content from the network?
- Nope, as soon as it hasn been requested it's in the wild (cf. faq#9).
- Once I add something to IPFS, will it remain available to others once I turn off my computer?
- IPNS: The Inter-Planetary Naming System