ashokpant

#!/bin/bash

# install CUDA Toolkit v9.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb)
CUDA_REPO_PKG="cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64-deb"
wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo apt-get update
sudo apt-get -y install cuda-9-0

codingPingjun

import pickle
import numpy as np
import pdb

img_width, img_height = 300, 300
box_configs = [
    {'layer_width': 38, 'layer_height': 38, 'num_prior': 3, 'min_size':  30.0,
     'max_size': None, 'aspect_ratios': [1.0, 2.0, 1/2.0]},
    {'layer_width': 19, 'layer_height': 19, 'num_prior': 6, 'min_size':  60.0,
     'max_size': 114.0, 'aspect_ratios': [1.0, 1.0, 2.0, 1/2.0, 3.0, 1/3.0]},

oysstu

def crc16(data: bytes, poly=0x8408):
    '''
    CRC-16-CCITT Algorithm
    '''
    data = bytearray(data)
    crc = 0xFFFF
    for b in data:
        cur_byte = 0xFF & b
        for _ in range(0, 8):
            if (crc & 0x0001) ^ (cur_byte & 0x0001):

udibr

# variation to https://github.com/ryankiros/skip-thoughts/blob/master/decoding/search.py

def keras_rnn_predict(samples, empty=empty, rnn_model=model, maxlen=maxlen):
    """for every sample, calculate probability for every possible label
    you need to supply your RNN model and maxlen - the length of sequences it can handle
    """
    data = sequence.pad_sequences(samples, maxlen=maxlen, value=empty)
    return rnn_model.predict(data, verbose=0)

def beamsearch(predict=keras_rnn_predict,

yrevar

{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',

tatome

 # coding=utf-8
#
#!/usr/bin/python
#
# Copyright 2013 Johannes Bauer, Universitaet Hamburg
#
# This file is free software.  Do with it whatever you like.
# It comes with no warranty, explicit or implicit, whatsoever.
#
# This python script implements an early version of Itti and Koch's

paulirish

Screencapture and animated gifs

I say "animated gif" but in reality I think it's irresponsible to be serving "real" GIF files to people now. You should be serving gfy's, gifv's, webm, mp4s, whatever. They're a fraction of the filesize making it easier for you to deliver high fidelity, full color animation very quickly, especially on bad mobile connections. (But I suppose if you're just doing this for small audiences (like bug reporting), then LICEcap is a good solution).

Capturing (Easy)

1. Launch quicktime player
2. do Screen recording