Trung Lê Hoàng trungkak

Install Terminator (shell)

sudo add-apt-repository ppa:gnome-terminator
sudo apt-get update
sudo apt-get install terminator

Terminator should be setup as default now. Restart your terminal (shortcut: "Ctrl+Alt+T").

Install ZSH

Prereq:

apt-get install zsh
apt-get install git-core

Getting zsh to work in ubuntu is weird, since sh does not understand the source command. So, you do this to install zsh

wget https://github.com/robbyrussell/oh-my-zsh/raw/master/tools/install.sh -O - | zsh

PostgreSQL Challenges

You need to build a pivot table WITHOUT using CROSSTAB function. Having two tables products and details you need to select a pivot table of products with counts of details occurrences (possible details values are ['good', 'ok', 'bad'].

SELECT
  distinct pro.name,
  (SELECT count(*) FROM details det2 WHERE det2.detail = 'good' AND det2.product_id = det.product_id) AS good,
  (SELECT count(*) FROM details det2 WHERE det2.detail = 'ok' AND det2.product_id = det.product_id) AS ok,
 (SELECT count(*) FROM details det2 WHERE det2.detail = 'bad' AND det2.product_id = det.product_id) AS bad

Neo4j Tutorial

Fundamentals

Store any kind of data using the following graph concepts:

Node: Graph data records
Relationship: Connect nodes (has direction and a type)
Property: Stores data in key-value pair in nodes and relationships
Label: Groups nodes and relationships (optional)

	from graphviz import Digraph
	import re
	import torch
	import torch.nn.functional as F
	from torch.autograd import Variable
	from torch.autograd import Variable
	import torchvision.models as models


	def make_dot(var):

	1.Create desktop config file

	sudo nano /usr/share/applications/smartsvn.desktop

	2. Put the text below

	[Desktop Entry]
	Name=SmartSVN
	Name[en]=SmartSVN
	Name[ru]=SmartSVN

	from __future__ import division
	import string
	import math

	tokenize = lambda doc: doc.lower().split(" ")

	document_0 = "China has a strong economy that is growing at a rapid pace. However politically it differs greatly from the US Economy."
	document_1 = "At last, China seems serious about confronting an endemic problem: domestic violence and corruption."
	document_2 = "Japan's prime minister, Shinzo Abe, is working towards healing the economic turmoil in his own country for his view on the future of his people."
	document_3 = "Vladimir Putin is working hard to fix the economy in Russia as the Ruble has tumbled."

	"""Sequence-to-sequence model with an attention mechanism."""
	# see https://www.tensorflow.org/versions/r0.10/tutorials/seq2seq/index.html
	# compare https://github.com/tflearn/tflearn/blob/master/examples/nlp/seq2seq_example.py
	from __future__ import print_function
	import numpy as np
	import tensorflow as tf

	vocab_size=256 # We are lazy, so we avoid fency mapping and just use one class per character/byte
	target_vocab_size=vocab_size
	learning_rate=0.1

	#!/bin/bash
	# install dependencies
	sudo apt-get update
	sudo apt-get install -y build-essential
	sudo apt-get install -y cmake
	sudo apt-get install -y libgtk2.0-dev
	sudo apt-get install -y pkg-config
	sudo apt-get install -y python-numpy python-dev
	sudo apt-get install -y libavcodec-dev libavformat-dev libswscale-dev
	sudo apt-get install -y libjpeg-dev libpng12-dev libtiff5-dev libjasper-dev

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)