shamatar

from keras.layers import Recurrent
import keras.backend as K
from keras import activations
from keras import initializers
from keras import regularizers
from keras import constraints
from keras.engine import Layer
from keras.engine import InputSpec

kylemcdonald

kastnerkyle

# -*- coding: utf-8 -*-
# Authors: jfsantos, Kyle Kastner
import sys
import numpy as np
import wave
import scipy as sp
from scipy.io import wavfile


def segment_axis(a, length, overlap=0, axis=None, end='cut', endvalue=0):

pca_animation.m

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Matlab code to produce PCA animations shown here:
% http://stats.stackexchange.com/questions/2691
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Static image

clear all
rng(42)

vyraun

wget https://repo.continuum.io/archive/Anaconda2-4.2.0-Linux-x86_64.sh
bash Anaconda2-4.2.0-Linux-x86_64.sh
export PATH=/home/viraun/anaconda2/bin:$PATH
pip install tensorflow keras

ili3p

local words = torch.load(opt.words) -- it's a tds.Hash
local word2vec = torch.FloatTensor(opt.vocabsz, opt.dim)
local buffsz = 2^13 -- == 8k
local f = io.input(opt.input)
local done = 0
local unk

-- read huge word2vec file with 2,196,017 lines
while true do
    local lines, leftover = f:read(buffsz, '*line')

wassname

"""
A keras attention layer that wraps RNN layers.

Based on tensorflows [attention_decoder](https://github.com/tensorflow/tensorflow/blob/c8a45a8e236776bed1d14fd71f3b6755bd63cc58/tensorflow/python/ops/seq2seq.py#L506) 
and [Grammar as a Foreign Language](https://arxiv.org/abs/1412.7449).

date: 20161101
author: wassname
url: https://gist.github.com/wassname/5292f95000e409e239b9dc973295327a
"""

awjuliani

morgangiraud

# Remove anything linked to nvidia
sudo apt-get remove --purge nvidia*
sudo apt-get autoremove

# Search for your driver
apt search nvidia

# Select one driver (the last one is a decent choice)
sudo apt install nvidia-370

mrtzh

"""Visualize stability of stochastic gradient descent for finding a linear
separator."""

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

np.random.seed(1)
mpl.rcParams['axes.linewidth'] = 0.0