singhay

# This is script is limited to single GPU at the moment due to LSH attention approximation
import argparse
import glob
import logging
import os
from typing import Dict, List

import torch
from reformer_pytorch import ReformerLM
from tokenizers import ByteLevelBPETokenizer

singhay

def get_model():

    input_image_seqs = Input(shape=(None, 32, 32, 1), name='input_image_seqs')
    bn1 = BatchNormalization()(input_image_seqs)
    ac1 = Activation('elu')(bn1)
    conv1 = TimeDistributed(Conv2D(32, (3, 3), strides=(1, 1), activation='elu', padding='same'))(ac1)
    bn2 = BatchNormalization()(conv1)
    ac2 = Activation('elu')(bn2)
    conv2 = TimeDistributed(Conv2D(32, (3, 3), strides=(1, 1), activation='elu', padding='same'))(ac2)

singhay

import os
import time
import string
import pickle

from operator import itemgetter

from nltk.corpus import stopwords as sw
from nltk.corpus import wordnet as wn
from nltk import wordpunct_tokenize

singhay

singhay

Pinterest: https://labs.pinterest.com/assets/paper/p2p-www17.pdf

Instacart: https://www.slideshare.net/SharathRao6/recommendation-systems-instacart

Netflix : http://dl.acm.org/citation.cfm?id=2843948

Youtube : http://dl.acm.org/citation.cfm?id=1864770 https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf

Spotify : http://benanne.github.io/2014/08/05/spotify-cnns.html

singhay

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA

'''
https://www.reddit.com/r/dataisbeautiful/comments/4c3zjt/cute_fish_out_of_mnist_handwritten_dataset_oc/
PCA Dimentionality Reduction of Handwritten Dataset from 784 to 2, normalizing and vizualizing.
'''
def main():

singhay

# Inspired from http://cs229.stanford.edu/materials/smo.pdf
import os
import numpy as np
import pandas as pd
import math
from sklearn.svm import LinearSVC
from sklearn.metrics import accuracy_score

def SMO(data, classLabel, constant, tolerance, maxpasses):
    dataMatrix = np.array(data)