mitmul

# install miniconda
cd
wget http://repo.continuum.io/miniconda/Miniconda-3.5.5-Linux-armv6l.sh
md5sum Miniconda-3.5.5-Linux-armv6l.sh
bash Miniconda-3.5.5-Linux-armv6l.sh -b
rm -rf Miniconda-3.5.5-Linux-armv6l.sh
echo 'export PATH=/home/pi/miniconda/bin:$PATH' >> .bashrc
source .bashrc
conda install pip --yes
conda install ipython --yes

kidpixo

Warning This is SEVERELY outdated, the current jupyter version is > 6.X, please refer to your current jupyter notebook installation!

Disclaimer : I just copied those shortcuts from Jupyter Menú > Help > Keyboard Shortcuts, I didn't wrote them myself.

Check your current shortcuts in your Help, shortcuts coule have been modified by extensions or your past self.

Toc

Keyboard shortcuts

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

xvitaly

@echo off
echo Uninstalling KB3075249 (telemetry for Win7/8.1)
start /w wusa.exe /uninstall /kb:3075249 /quiet /norestart
echo Uninstalling KB3080149 (telemetry for Win7/8.1)
start /w wusa.exe /uninstall /kb:3080149 /quiet /norestart
echo Uninstalling KB3021917 (telemetry for Win7)
start /w wusa.exe /uninstall /kb:3021917 /quiet /norestart
echo Uninstalling KB3022345 (telemetry)
start /w wusa.exe /uninstall /kb:3022345 /quiet /norestart
echo Uninstalling KB3068708 (telemetry)

ernestum

import numpy as np
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.
    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in
       Proc. of the International Conference on Document Analysis and
       Recognition, 2003.

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

codekansas

from __future__ import print_function

import json
import os
import numpy as np

from gensim.models import Word2Vec
from gensim.utils import simple_preprocess
from keras.engine import Input
from keras.layers import Embedding, merge

udibr

import numpy as np
from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments

cbaziotis

def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':

donaldmunro

#include <eigen3/Eigen/Dense>
#include <eigen3/Eigen/Eigenvalues>
..
..
using RowMatrixXf = Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
Eigen::Map<RowMatrixXf> mapped(extended_homograpy.ptr<float>(), homography.rows, homography.cols);
Eigen::EigenSolver<Eigen::MatrixXf> eigen_solve(mapped, true);
auto ev = eigen_solve.eigenvectors();
std::cout << "No of EigenVectors = " << ev.cols() << std::endl;
for (auto i=0; i<ev.cols(); i++)