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)

grugq

Operational PGP

This is a guide on how to email securely.

There are many guides on how to install and use PGP to encrypt email. This is not one of them. This is a guide on secure communication using email with PGP encryption. If you are not familiar with PGP, please read another guide first. If you are comfortable using PGP to encrypt and decrypt emails, this guide will raise your security to the next level.

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

nasbench

# Source: System.Management.Automation.dll
# This list is used to determin if a ScriptBlock contains potential suspicious content
# If a match is found an automatic 4104 with a "warning" level is generated.
# https://github.com/PowerShell/PowerShell/blob/master/src/System.Management.Automation/engine/runtime/CompiledScriptBlock.cs
- "Add-Type"
- "AddSecurityPackage"
- "AdjustTokenPrivileges"
- "AllocHGlobal"
- "BindingFlags"
- "Bypass"

baraldilorenzo

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

OnlyInAmerica

# Find the IAM username belonging to the TARGET_ACCESS_KEY
# Useful for finding IAM user corresponding to a compromised AWS credential

# Requirements:
#
# Environmental variables: 
# 		AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY
# python: 
#		boto

mscharhag

import java.time.*;
import java.time.format.DateTimeFormatter;
import java.time.format.FormatStyle;
import java.time.temporal.ChronoUnit;
import java.time.temporal.TemporalAdjusters;
import java.util.*;

import static java.time.temporal.TemporalAdjusters.*;

public class Java8DateTimeExamples {

dezhub

function Get-ClrReflection
{
    <# 
    .SYNOPSIS 
    
    Detects memory-only CLR (.NET) modules
     
    Author: Joe Desimone (@dez_) 
    License: BSD 3-Clause