nadavrot

High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of

abyoussef

import tensorflow as tf
import numpy as np
import os

import zconfig
import utils


class RBM(object):

hiropppe

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.linear_model import SGDClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import cross_val_score
from jpjplearn.datasets import load_clf_corpus
from jpjplearn.analyzer import mecab_analyzer

hiropppe

import numpy as np

from pyspark.sql import Row, SQLContext

from pyspark.mllib.feature import HashingTF
from pyspark.mllib.feature import IDF
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.classification import SVMWithSGD, SVMModel
from pyspark.mllib.classification import NaiveBayes, NaiveBayesModel

syhw

"""
A deep neural network with or w/o dropout in one file.

License: Do What The Fuck You Want to Public License http://www.wtfpl.net/
"""

import numpy, theano, sys, math
from theano import tensor as T
from theano import shared
from theano.tensor.shared_randomstreams import RandomStreams

parosky

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import csv
import codecs
import numpy as np
import MeCab

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans, MiniBatchKMeans