Motion, keyboard commands and shortcuts for VI Improved
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#!/bin/bash | |
## This gist contains step by step instructions to install cuda v9.0 and cudnn 7.2 in ubuntu 18.04 | |
### steps #### | |
# verify the system has a cuda-capable gpu | |
# download and install the nvidia cuda toolkit and cudnn | |
# setup environmental variables | |
# verify the installation | |
### |
motion | description |
---|---|
h | Count characters left |
l | Count characters right |
^ | To the first character of the line |
$ | To the last character of the line |
f<char> | To the counth character occurrence to the right. F<char> to the counth character occurrence to the left |
t<char> | To 1 character just before the counth character occurrence to the right |
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#!/bin/bash | |
NVIDIA_DRIVER_VERSION=352.63 | |
NVIDIA_CUDA_VERSION=7.5 | |
NVIDIA_CUDA_FULL_VERSION=7.5.18 | |
sudo yum update -y | |
sudo yum groupinstall -y "Development tools" | |
sudo yum install kernel-devel-`uname -r` |
##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
##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