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PharrellWANG / labels_1024.tsv
Created May 10, 2017 13:42 — forked from teamdandelion/labels_1024.tsv
TensorBoard: TF Dev Summit Tutorial
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@PharrellWANG
PharrellWANG / jupyter_shortcuts.md
Created June 5, 2017 11:24 — forked from kidpixo/jupyter_shortcuts.md
Keyboard shortcuts for ipython notebook 3.1.0 / jupyter

Toc

Keyboard shortcuts

The IPython Notebook has two different keyboard input modes. Edit mode allows you to type code/text into a cell and is indicated by a green cell border. Command mode binds the keyboard to notebook level actions and is indicated by a grey cell border.

MacOS modifier keys:

  • ⌘ : Command
@PharrellWANG
PharrellWANG / jupyter_shortcuts.md
Created June 5, 2017 11:24 — forked from kidpixo/jupyter_shortcuts.md
Keyboard shortcuts for ipython notebook 3.1.0 / jupyter

Toc

Keyboard shortcuts

The IPython Notebook has two different keyboard input modes. Edit mode allows you to type code/text into a cell and is indicated by a green cell border. Command mode binds the keyboard to notebook level actions and is indicated by a grey cell border.

MacOS modifier keys:

  • ⌘ : Command
@PharrellWANG
PharrellWANG / autopgsqlbackup
Created July 21, 2017 08:27 — forked from matthewlehner/autopgsqlbackup
Auto PostgreSQL backup script.
#!/bin/bash
#
# PostgreSQL Backup Script Ver 1.0
# http://autopgsqlbackup.frozenpc.net
# Copyright (c) 2005 Aaron Axelsen <axelseaa@amadmax.com>
#
# This script is based of the AutoMySQLBackup Script Ver 2.2
# It can be found at http://sourceforge.net/projects/automysqlbackup/
#
# The PostgreSQL changes are based on a patch agaisnt AutoMySQLBackup 1.9
@PharrellWANG
PharrellWANG / gist:d9ed0be04de8f523032061120c922f92
Created November 20, 2017 03:44 — forked from rxaviers/gist:7360908
Complete list of github markdown emoji markup

People

:bowtie: :bowtie: 😄 :smile: 😆 :laughing:
😊 :blush: 😃 :smiley: ☺️ :relaxed:
😏 :smirk: 😍 :heart_eyes: 😘 :kissing_heart:
😚 :kissing_closed_eyes: 😳 :flushed: 😌 :relieved:
😆 :satisfied: 😁 :grin: 😉 :wink:
😜 :stuck_out_tongue_winking_eye: 😝 :stuck_out_tongue_closed_eyes: 😀 :grinning:
😗 :kissing: 😙 :kissing_smiling_eyes: 😛 :stuck_out_tongue:

STEPS

  • Click on Help menu

  • Select Enter License

  • Then paste given KEY given at bottom

  • Finally click on Use License

@PharrellWANG
PharrellWANG / InstallOpenCV.md
Created August 29, 2018 02:56 — forked from jruizvar/InstallOpenCV.md
Building OpenCV 3.2.0 from source on macOS Sierra with Python 3 support

Building OpenCV 3.2.0 from source with Python 3 support

Install OpenCV on macOS Sierra enabling Python 3 with the following instructions:

  • Install CMake, Python 3 + Numpy in advance
  • Download latest OpenCV source code (https://github.com/opencv/opencv/releases)
  • Move the folder opencv-3.2.0 to the current directory
  • In the current directory, execute the following steps:
mkdir build
Enterprise: NJVYC-BMHX2-G77MM-4XJMR-6Q8QF
Professional: KBJFW-NXHK6-W4WJM-CRMQB-G3CDH
Keys are generic ones. These are the same from MSDN account.
Product Key : -6Q8QF
Validity : Valid
Product ID : 00369-90000-00000-AA703
Advanced ID : XXXXX-03699-000-000000-00-1032-9200.0000-0672017
@PharrellWANG
PharrellWANG / doit.sh
Created November 3, 2018 03:11 — forked from charlesreid1/doit.sh
Download the Large-scale CelebFaces Attributes (CelebA) Dataset from their Google Drive link
#!/bin/bash
#
# Download the Large-scale CelebFaces Attributes (CelebA) Dataset
# from their Google Drive link.
#
# CelebA: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
#
# Google Drive: https://drive.google.com/drive/folders/0B7EVK8r0v71pWEZsZE9oNnFzTm8
python3 get_drive_file.py 0B7EVK8r0v71pZjFTYXZWM3FlRnM celebA.zip
@PharrellWANG
PharrellWANG / tf.py
Created November 23, 2018 02:54 — forked from koaning/tf.py
tensorflow layer example
import tensorflow as tf
import numpy as np
import uuid
x = tf.placeholder(shape=[None, 3], dtype=tf.float32)
nn = tf.layers.dense(x, 3, activation=tf.nn.sigmoid)
nn = tf.layers.dense(nn, 5, activation=tf.nn.sigmoid)
encoded = tf.layers.dense(nn, 2, activation=tf.nn.sigmoid)
nn = tf.layers.dense(encoded, 5, activation=tf.nn.sigmoid)
nn = tf.layers.dense(nn, 3, activation=tf.nn.sigmoid)