sidgan

#A Collection of NLP notes

##N-grams

###Calculating unigram probabilities:

P( w_i ) = count ( w_i ) ) / count ( total number of words )

In english..

sidgan

# Author: Kyle Kastner# License: BSD 3-Clause# For a reference on parallel processing in Python see tutorial by David Beazley# http://www.slideshare.net/dabeaz/an-introduction-to-python-concurrency# Loosely based on IBM example# http://www.ibm.com/developerworks/aix/library/au-threadingpython/# If you want to download all the PASCAL VOC data, use the following in bash..."""#! /bin/bash# 2008wget http://host.robots.ox.ac.uk/pascal/VOC/voc2008/VOCtrainval_14-Jul-2008.tar# 2009wget http://host.robots.ox.ac.uk/pascal/VOC/voc2009/VOCtrainval_11-May-2009.tar# 2010wget http://host.robots.ox.ac.uk/pascal/VOC/voc2010/VOCtrainval_03-May-2010.tar# 2011wget http://host.robots.ox.ac.uk/pascal/VOC/voc2011/VOCtrainval_25-May-2011.tar# 2012wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar# Latest devkitwget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCdevkit_18-May-2011.tar"""try:    import Queueexcept ImportError:    import queue as Queueimport threadingimport ti

sidgan

import cv2
import string, random


vc = cv2.VideoCapture(0)

if vc.isOpened(): # try to get the first frame
    rval, frame = vc.read()
else:
    rval = False

sidgan

# MIT License
# 
# Copyright (c) 2016 David Sandberg
# 
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

sidgan

"""Keras implementation of SSD."""

import keras.backend as K
from keras.layers import Activation
from keras.layers import AtrousConv2D
from keras.layers import Conv2D
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import GlobalAveragePooling2D
from keras.layers import Input

sidgan

[?1034h{
  protos : 
    {
      cnn : 
        {
          gradInput : FloatTensor - empty
          modules : 
            {
              1 : 
                {

sidgan

#####
# modifiled from https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/06_CIFAR-10.ipynb
#####
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from sklearn.metrics import confusion_matrix
import time
from datetime import timedelta
import math

sidgan

My problems with the paper:
- There is no comparison of resulting video quality. The amount of encode time (and power
  expended) to produce a H.264 bit stream *dramatically* depends on the desired quality level;
  e.g. for x264 (state of the art SW encoder, already in 2010 when the paper was written), the
  difference between the fastest and best quality settings is close to 2 orders of magnitude
  in both speed and power use. This is not negligible!
  [NOTE: This is excluding quality-presets like "placebo", which are more demanding still.
  Even just comparing between different settings usable for real-time encoding, we still have
  at least an order of magnitude difference.]
- They have their encoder, which is apparently based on JM 8.6 (*not* a good encoder!), for

sidgan

Key-Value Memory Networks for Directly Reading Documents

Introduction

• Knowledge Bases (KBs) are effective tools for Question Answering (QA) but are often too restrictive (due to fixed schema) and too sparse (due to limitations of Information Extraction (IE) systems).
• The paper proposes Key-Value Memory Networks, a neural network architecture based on Memory Networks that can leverage both KBs and raw data for QA.
• The paper also introduces MOVIEQA, a new QA dataset that can be answered by a perfect KB, by Wikipedia pages and by an imperfect KB obtained using IE techniques thereby allowing a comparison between systems using any of the three sources.
• Link to the paper.

Related Work

sidgan

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