| $ git clone git@github.com:xxxxx/xxxx.git my-awesome-proj | |
| Cloning into 'my-awesome-proj'... | |
| ssh: connect to host github.com port 22: Connection timed out | |
| fatal: Could not read from remote repository. | |
| $ # This should also timeout | |
| $ ssh -T git@github.com | |
| ssh: connect to host github.com port 22: Connection timed out | |
| $ # but this might work |
| {0: 'tench, Tinca tinca', | |
| 1: 'goldfish, Carassius auratus', | |
| 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', | |
| 3: 'tiger shark, Galeocerdo cuvieri', | |
| 4: 'hammerhead, hammerhead shark', | |
| 5: 'electric ray, crampfish, numbfish, torpedo', | |
| 6: 'stingray', | |
| 7: 'cock', | |
| 8: 'hen', | |
| 9: 'ostrich, Struthio camelus', |
| #!/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 | |
| ### |
| /** | |
| * Example to refresh tokens using https://github.com/auth0/node-jsonwebtoken | |
| * It was requested to be introduced at as part of the jsonwebtoken library, | |
| * since we feel it does not add too much value but it will add code to mantain | |
| * we won't include it. | |
| * | |
| * I create this gist just to help those who want to auto-refresh JWTs. | |
| */ | |
| const jwt = require('jwt'); |
| /** | |
| * Example to refresh tokens using https://github.com/auth0/node-jsonwebtoken | |
| * It was requested to be introduced at as part of the jsonwebtoken library, | |
| * since we feel it does not add too much value but it will add code to mantain | |
| * we won't include it. | |
| * | |
| * I create this gist just to help those who want to auto-refresh JWTs. | |
| */ | |
| const jwt = require('jwt'); |
##VGG16 model for Keras
This is the Keras model of the 16-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
| """Script to illustrate usage of tf.estimator.Estimator in TF v1.3""" | |
| import tensorflow as tf | |
| from tensorflow.examples.tutorials.mnist import input_data as mnist_data | |
| from tensorflow.contrib import slim | |
| from tensorflow.contrib.learn import ModeKeys | |
| from tensorflow.contrib.learn import learn_runner | |
| # Show debugging output |
Getting Setup: Follow the instruction on https://gym.openai.com/docs
git clone https://github.com/openai/gym
cd gym
pip install -e . # minimal install
Basic Example using CartPole-v0:
| ##################################################### | |
| # Name: Heroku CLI CheatSheet | |
| # Author: J. Le Coupanec | |
| # Date: 2017/10/03 | |
| # https://devcenter.heroku.com/articles/heroku-cli | |
| ##################################################### | |
| heroku access # manage user access to apps | |
| heroku addons # tools and services for developing, extending, and operating your app |