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@erogol
erogol / ddc_sentece_test_330k.ipynb
Last active May 14, 2021 09:40
DDC_sentece_test_330K.ipynb
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@erogol
erogol / tts_example.ipynb
Last active July 5, 2024 04:27
TTS_example.ipynb
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@hitvoice
hitvoice / plot_confusion_matrix.png
Last active October 16, 2024 13:24
Generate matrix plot for confusion matrix with pretty annotations.
plot_confusion_matrix.png
@lukas-h
lukas-h / license-badges.md
Last active November 11, 2024 03:22
Markdown License Badges for your Project

Markdown License badges

Collection of License badges for your Project's README file.
This list includes the most common open source and open data licenses.
Easily copy and paste the code under the badges into your Markdown files.

Notes

  • The badges do not fully replace the license informations for your projects, they are only emblems for the README, that the user can see the License at first glance.

Translations: (No guarantee that the translations are up-to-date)

@erogol
erogol / maxout_layer
Created January 19, 2015 14:03
maxout layer implementation for caffe library
layers {
name: "conv1A"
type: CONVOLUTION
bottom: "data"
top: "conv1A"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
@erogol
erogol / bing_scrap.py
Created May 29, 2014 10:07
bing scrabber but add more to query address
#!/bin/bash
from bs4 import BeautifulSoup
import requests
import urllib2
import os
import re, urlparse
import time
import pdb
from interruptingcow import timeout
@erogol
erogol / twitter_scrapper.py
Created December 25, 2013 22:34
scrap twitter wall given the search page address or simulating a browser via mechanize
'''
Written by:
Eren Golge - [email protected]
'''
import json
import pdb
import urllib2
import mechanize
import cookielib
@erogol
erogol / crawle_google.py
Created September 22, 2013 10:57
scrap google images given the target query
#!/usr/bin/env python
'''
Query on GoogleImageSearch and install resulted images by scraping.
To use this script install mechanize and BeautifulSoup packages as
easy_install mechanize
easy_install Beautiful
import numpy as np
from scipy import linalg
from sklearn.utils import array2d, as_float_array
from sklearn.base import TransformerMixin, BaseEstimator
class ZCA(BaseEstimator, TransformerMixin):
def __init__(self, regularization=10**-5, copy=False):
self.regularization = regularization

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