trungkak

"""
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)

trungkak

# 1. Install CUDA
# Preparation
sudo apt-get update
sudo apt-get upgrade
sudo apt-get install tmux build-essential gcc g++ make binutils
sudo apt-get install software-properties-common

# Download Cuda toolkit (Nvidia driver included)
cd ~/Downloads
# Download CUDA 8.0 from this https://developer.nvidia.com/cuda-80-ga2-download-archive, not 9.0

trungkak

#!/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

trungkak

$ sudo apt-get install build-essential cmake
$ sudo apt-get install libgtk-3-dev
$ sudo apt-get install libboost-all-dev

$ pip install numpy
$ pip install scipy
$ pip install scikit-image
$ pip install dlib

#Locate libmkl_rt.so

trungkak

for dir in $(find . -type d)
do
  if [ $(ls -l "$dir" | grep '\.jpg$' | wc -l) -ge 3 ]
  then
    cp -r $dir ../slfw
  fi
done

trungkak

Pattern pattern = Pattern.compile(
                "# Match a sentence ending in punctuation or EOS.\n" +
                        "[^.!?\\s]    # First char is non-punct, non-ws\n" +
                        "[^.!?]*      # Greedily consume up to punctuation.\n" +
                        "(?:          # Group for unrolling the loop.\n" +
                        "  [.!?]      # (special) inner punctuation ok if\n" +
                        "  (?!['\"]?\\s|$)  # not followed by ws or EOS.\n" +
                        "  [^.!?]*    # Greedily consume up to punctuation.\n" +
                        ")*           # Zero or more (special normal*)\n" +
                        "[.!?]?       # Optional ending punctuation.\n" +

trungkak

"""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

trungkak

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)

trungkak

PostgreSQL Challenges

1. 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

trungkak

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."