endolith

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٩(̾●̮̮̃̾•̃̾)۶

zcshiner

#!/bin/bash

### Command log to install Cuda Toolkit 6.5, driver 343.22, and ccminer.

## Update the system
sudo apt-get update && sudo apt-get -y dist-upgrade
# All the dependencies for Cuda & ccminer (I think)
sudo apt-get -y install gcc g++ build-essential automake linux-headers-$(uname -r) git gawk libcurl4-openssl-dev libjansson-dev xorg libc++-dev libgmp-dev python-dev
 

jimgoo

name: CaffeNet fine-tuned on the Oxford 102 category flower dataset

caffemodel: oxford102.caffemodel

caffemodel_url: https://s3.amazonaws.com/jgoode/oxford102.caffemodel

gist_id: 0179e52305ca768a601f

license: BSD-3

atcuno

The purpose of this document is to make recommendations on how to browse in a privacy and security conscious manner. This information is compiled from a number of sources, which are referenced throughout the document, as well as my own experiences with the described technologies.

I welcome contributions and comments on the information contained. Please see the How to Contribute section for information on contributing your own knowledge.

Table of Contents

• Motivation
• Audience
• TL;DR?
• Updates
• Technologies that Affect Security and Privacy

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)

HelloZeroNet

import urllib2, re, json, os, time, sys, HTMLParser

html_parser = HTMLParser.HTMLParser()

auth_address = "1KbV1e1u6P6AsY8XNBydgtbtN8iSB5WMyG"
auth_privatekey = "xxxx"
site = "1TaLkFrMwvbNsooF4ioKAY9EuxTBTjipT"
zeronet_dir = ".."

os.chdir(zeronet_dir)

htoyryla

-- this program takes an original image, such as a photo,
-- and a generated image, such as generated by jcjohnson/fast-neural-style
-- and copies the original colors to the generated image
-- like when using the original_colors param in jcjohnson/neural-style
--
-- by hannu töyrylä @htoyryla 30 oct 2016
--

require 'torch'
require 'image'

teamdandelion

7
2
1
0
4
1
4
9
5
9

etherealvisage

#!/bin/sh
IP=192.168.2.200
docker run -d -p $IP:8019:8019 voxhub/silvius-worker:latest /bin/sh -c 'cd /root/silvius-backend ; python kaldigstserver/master_server.py'
docker run -d voxhub/silvius-worker /root/worker.sh -u ws://$IP:8019/worker/ws/speech

SuperShinyEyes

def f1_loss(y_true:torch.Tensor, y_pred:torch.Tensor, is_training=False) -> torch.Tensor:
    '''Calculate F1 score. Can work with gpu tensors
    
    The original implmentation is written by Michal Haltuf on Kaggle.
    
    Returns
    -------
    torch.Tensor
        `ndim` == 1. 0 <= val <= 1