robertknight

Switching from Mendeley Desktop 1.13 to an earlier version

Since this is a major update, it is unfortunately not backwards compatible with Mendeley Desktop 1.12. If you encounter problems with the preview release and need to go back to 1.12 whilst we fix the issue, please read this carefully.

In particular do not use the 'Restore Backup' feature for this.

Before you go back to Mendeley Desktop 1.12

yobibyte

import csv
import urllib
from bs4 import BeautifulSoup

url_to_parse = 'https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018'
page = urllib.request.urlopen(url_to_parse)
soup = BeautifulSoup(page, 'html.parser')

with open('proceedings.csv', 'w') as csvfile:
    wr = csv.writer(csvfile, delimiter=',')

h4cc

#
# Ubuntu 16.06 LTS (Xenial Xerus)
#
# Basic packages i usually install.
#
# Author: Julius Beckmann <github@h4cc.de>
#
# Upgraded Script from 14.04: https://gist.github.com/h4cc/7be7f940325614dc59fb
#

tvercaut

Hack file to get a nicer Gist name

Newmu

import os
import numpy as np
from matplotlib import pyplot as plt
from time import time

from foxhound import activations
from foxhound import updates
from foxhound import inits
from foxhound.theano_utils import floatX, sharedX

mamemomonga

HHKB-BTをHIDとして認識させる(失敗)

HID化はうまくいかなかったけど、bluetoothctl で普通のHCIデバイスとしての設定メモとして残しておく。

参考資料: BIOS 画面でもBluetooth LE な無線キーボードを使いたい

使用デバイス

Raspberry Pi 3

gabrieleangeletti

import tensorflow as tf
import numpy as np
import os

import zconfig
import utils


class RBM(object):

josemarcosrf

CUDA_REPO_PKG=cuda-repo-ubuntu1804_10.2.89-1_amd64.deb
CUDA_URL=http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64

wget -O /tmp/${CUDA_REPO_PKG} ${CUDA_URL}/${CUDA_REPO_PKG} 

sudo dpkg -i /tmp/${CUDA_REPO_PKG}

sudo apt-key adv --fetch-keys ${CUDA_URL}/7fa2af80.pub 

rm -f /tmp/${CUDA_REPO_PKG}

hrshovon

This is a note on how to cross compile opencv for pretty much any ARM device(HardFP supported in this case) and deploy. Native
compiling in ARM devices can be painfully slow and they seem to hang often during build(mine got stuck at 43%). So if you happen
to have a desktop/laptop/server running ubuntu or similar linux distro, u can build opencv in fractionth of the time taken for 
native compiling without any issues.
Building opencv3 with TBB and NEON and VFP support can boost opencv performance. Thanks to Adrian at pyimagesearch for pointing 
that out.
Both my PC and target machine aka orange pi zero are running ubuntu 16.04 with python2.7 and python 3.5.
Let us use the term "build machine" for your PC where you are building opencv and "target machine" for the ARM single board computer.

1.Run the following commands in both machines(I think installing these in target machine only would do) to install the necessary libraries etc.(mine worked with them,so they should be enough 

jinglescode

import torch
import torch.nn as nn

class conv_block_nested(nn.Module):

    def __init__(self, in_ch, mid_ch, out_ch):
        super(conv_block_nested, self).__init__()
        self.activation = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv2d(in_ch, mid_ch, kernel_size=3, padding=1, bias=True)
        self.bn1 = nn.BatchNorm2d(mid_ch)