calroc

from itertools import product


phi = frozenset()
y = frozenset({''})

syms = O, l = frozenset({'0'}), frozenset({'1'})

AND, CONS, KSTAR, NOT, OR = 'and cons * not or'.split()

swalkinshaw

Moved to Shopify/graphql-design-tutorial

binaryfoundry

from keras.models import Sequential
from keras.layers import Dense
import numpy as np

np.random.seed(7)

NUM_DIGITS = 12

def binary_encode(i, num_digits):
    return np.array([i >> d & 1 for d in range(num_digits)])

harv

#! /bin/sh

# update glibc to 2.17 for CentOS 6

wget http://copr-be.cloud.fedoraproject.org/results/mosquito/myrepo-el6/epel-6-x86_64/glibc-2.17-55.fc20/glibc-2.17-55.el6.x86_64.rpm
wget http://copr-be.cloud.fedoraproject.org/results/mosquito/myrepo-el6/epel-6-x86_64/glibc-2.17-55.fc20/glibc-common-2.17-55.el6.x86_64.rpm
wget http://copr-be.cloud.fedoraproject.org/results/mosquito/myrepo-el6/epel-6-x86_64/glibc-2.17-55.fc20/glibc-devel-2.17-55.el6.x86_64.rpm
wget http://copr-be.cloud.fedoraproject.org/results/mosquito/myrepo-el6/epel-6-x86_64/glibc-2.17-55.fc20/glibc-headers-2.17-55.el6.x86_64.rpm

sudo rpm -Uvh glibc-2.17-55.el6.x86_64.rpm \

shibuiwilliam

import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils
from keras.layers import Dense
from keras.models import Model


# create training data
def binary_encode(i, num_digits):

psyyz10

This document summarizes some potentially useful papers and code repositories on Sentiment analysis / document classification

• CNN:

  1. Related Paper: Convolutional Neural Networks for Sentence Classification. EMNLP 2014

     A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

     Code:

dirko

# Keras==1.0.6
from keras.models import Sequential
import numpy as np
from keras.layers.recurrent import LSTM
from keras.layers.core import TimeDistributedDense, Activation
from keras.preprocessing.sequence import pad_sequences
from keras.layers.embeddings import Embedding
from sklearn.cross_validation import train_test_split
from keras.layers import Merge
from keras.backend import tf

alvations

NLTK API to Stanford NLP Tools compiled on 2015-12-09

Stanford NER

With NLTK version 3.1 and Stanford NER tool 2015-12-09, it is possible to hack the StanfordNERTagger._stanford_jar to include other .jar files that are necessary for the new tagger.

First set up the environment variables as per instructed at https://github.com/nltk/nltk/wiki/Installing-Third-Party-Software

svpino

// Programming challenge: rotating a matrix 90 degrees in place
// Original post: https://blog.svpino.com/2015/05/10/programming-challenge-rotating-a-matrix-90-degrees-in-place

public class RotatingMatrix90DegreesInPlace {

    private static int[][] matrix = { 
        { 1, 2, 3, 4 }, 
        { 5, 6, 7, 8 }, 
        { 9, 10, 11, 12 }, 
        { 13, 14, 15, 16 } 

StevenMaude

#!/usr/bin/env python
# encoding: utf-8
import codecs
import os
import sys

import numpy as np

from sklearn.feature_extraction.text import TfidfVectorizer