Meet Shah meetps

The Batch Normalization paper describes a method to address the various issues related to training of Deep Neural Networks. It makes normalization a part of the architecture itself and reports significant improvements in terms of the number of iterations required to train the network.

Issues With Training Deep Neural Networks

Internal Covariate shift

Covariate shift refers to the change in the input distribution to a learning system. In the case of deep networks, the input to each layer is affected by parameters in all the input layers. So even small changes to the network get amplified down the network. This leads to change in the input distribution to internal layers of the deep network and is known as internal covariate shift.

It is well established that networks converge faster if the inputs have been whitened (ie zero mean, unit variances) and are uncorrelated and internal covariate shift leads to just the opposite.

Using Python 3 + Google Cloud Vision API's OCR to extract text from photos and scanned documents

Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.

The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.

On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:

####### 1. A low-resolution photo of road signs

How to add an image to a gist

Create or find a gist that you own.

Clone your gist (replace <hash> with your gist's hash):

# with ssh
git clone [email protected]:<hash>.git mygist

# with https

git clone https://gist.github.com/.git mygist

	#!/bin/python

	# Dependencies:
	# pip install flask
	# pip install redis

	from flask import Flask
	from flask import request
	import flask
	import redis

	import pandas as pd
	from random import random

	flow = (list(range(1,10,1)) + list(range(10,1,-1)))*100
	pdata = pd.DataFrame({"a":flow, "b":flow})
	pdata.b = pdata.b.shift(9)
	data = pdata.iloc[10:] * random() # some noise

	import numpy as np

	FROM ubuntu:trusty

	########################################
	#
	# openslide 3.4.1 image based on Ubuntu:trusty
	#
	#######################################

	# Set Locale

	from math import sqrt

	def put_kernels_on_grid (kernel, pad = 1):

	'''Visualize conv. filters as an image (mostly for the 1st layer).
	Arranges filters into a grid, with some paddings between adjacent filters.

	Args:
	kernel: tensor of shape [Y, X, NumChannels, NumKernels]
	pad: number of black pixels around each filter (between them)

	"""Print most frequent N-grams in given file.

	Usage: python ngrams.py filename

	Problem description: Build a tool which receives a corpus of text,
	analyses it and reports the top 10 most frequent bigrams, trigrams,
	four-grams (i.e. most frequently occurring two, three and four word
	consecutive combinations).

	NOTES

	sudo pacman -S ruby ruby-rdoc gcc make

	gem update --user-install
	gem install jekyll --user-install

	# finally add $HOME/.gem/ruby/2.7.0/bin to your PATH variable