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)

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

williballenthin

# generate via: wevtutil gp Microsoft-Windows-Sysmon /getevents /getmessage

name: Microsoft-Windows-Sysmon
guid: 5770385f-c22a-43e0-bf4c-06f5698ffbd9
helpLink: 
resourceFileName: C:\Windows\Sysmon.exe
messageFileName: C:\Windows\Sysmon.exe
message: 
channels:
  channel:

dezhub

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

eneldoserrata

Hi.

Here is the content of my nginx config file :

location /analytics {
            proxy_set_header Host $host;
            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
            proxy_set_header X-Script-Name /analytics;
            proxy_pass http://YOUR_SERVER_NAME:8088;
            #YOUR_SERVER_NAME is localhost if both nginx and superset run on same server

nma-io

Install

Grab a copy of Docker for your platform here: https://www.docker.com/community-edition#/download Follow the installation guide and tune the docker system to run with as much memory and CPU as you're willing to feed to it.

Docker Containers I find useful for general security tasks:

Local Debian instance: debian:latest

Metasploit: remnux/metasploit

coffeetocode

$ ./try_local_metadata.sh
Trying 169.254.169.254...             found metadata
Trying 169.254.43518...               found metadata
Trying 169.16689662...                found metadata
Trying 2852039166...                  found metadata
Trying 0251.0376.0251.0376...         found metadata
Trying 0251.0376.0124776...           found metadata
Trying 251.0775248...                 -
Trying 25177524776...                 -
Trying 0xa9.0xfe.0xa9.0xfe...         found metadata

gilrosenthal

!pip install fastai
!apt-get -qq install -y libsm6 libxext6 && pip install -q -U opencv-python
import cv2
from os import path
from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag
platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())

accelerator = 'cu80' if path.exists('/opt/bin/nvidia-smi') else 'cpu'

!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.3.0.post4-{platform}-linux_x86_64.whl torchvision