mir4ef

git rm --cached file-name.txt
git rm -r --cached folder-name
git commit -m "removed file-name and/or folder-name"
git push origin master or branch-name

MaximumEntropy

import subprocess
import sys

tokenizer_path = sys.argv[1] # Path to the moses tokenizer mosesdecoder/scripts/tokenizer.perl
text = sys.argv[2] # Text to be tokenized
lang = sys.argv[3] # Input language ex: en, fr, de

pipe = subprocess.Popen(["perl", tokenizer_path, '-l', lang, text], stdin=subprocess.PIPE, stdout=subprocess.PIPE)
pipe.stdin.write(text.encode('utf-8'))
pipe.stdin.close()

ameasure

from keras.engine.topology import Layer
from keras import initializations
from keras import backend as K

class Attention(Layer):
    '''Attention operation for temporal data.
    # Input shape
        3D tensor with shape: `(samples, steps, features)`.
    # Output shape
        2D tensor with shape: `(samples, features)`.

mastazi

# This is inspired by the fantastic guide https://github.com/saiprashanths/dl-setup
# I have just updated the python-related commands so that everything works in Python 3.
# Tested on Xubuntu 16.04.

# First of all let's update the repos:
sudo apt-get update 

# Only if you have a CUDA-compatible Nvidia card, install CUDA.
# Check on the Nvidia website what is the latest driver version which supports your card. 
# At the time of this writing it was 367.

georgepar

#!/usr/bin/env python3
"""
Credits https://github.com/ivanychev/learning/blob/master/Python/ipynb2pdf/ipynb2pdf.py

Current version of Jupyter doesn't support pdf exporting when it comes to 
greek language in the document. To fix this, current script has born.
It requires nbconvert as long as jupyter to be installed.


Author:     Sergey Ivanychev

tokestermw

from __future__ import print_function

from keras import backend as K
from keras.engine import Input, Model, InputSpec
from keras.layers import Dense, Activation, Dropout, Lambda
from keras.layers import Embedding, LSTM
from keras.optimizers import Adam
from keras.preprocessing import sequence
from keras.utils.data_utils import get_file
from keras.datasets import imdb

fchollet

import keras
import numpy as np

timesteps = 60
input_dim = 64
samples = 10000
batch_size = 128
output_dim = 64

# Test data.

filitchp

This is a guide for installing OpenCV 3.1 on Ubuntu 16.04 with Cuda 8 support. This has been tested using a system with a GeForce GTX 1060 and on one with a GeForce GTX 1080.

Nvidia Drivers with Compiz

Install Nvidia drivers

# Start clean
sudo apt purge nvidia-*
# Add the PPA
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update

njellinas

import keras.backend as K
from keras.layers import Layer
from keras.legacy import interfaces
from keras.engine import InputSpec
from keras import activations, initializers, regularizers, constraints


class DenseTransposeTied(Layer):

    @interfaces.legacy_dense_support

ptrblck

import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F

import matplotlib.pyplot as plt

import numpy as np