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Exploring nature beyond Kármán line

Gurkirat Singh tbhaxor

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Exploring nature beyond Kármán line
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tbhaxor / radare.md
Created April 27, 2019 19:07
Radare 2 Cheat Sheet

Radare2

Command line options

-L: List of supported IO plugins
-q: Exit after processing commands
-w: Write mode enabled
-i [file]: Interprets a r2 script
-A: Analyze executable at load time (xrefs, etc)
-n: Bare load. Do not load executable info as the entrypoint
@tbhaxor
tbhaxor / Vagrantfile
Created May 4, 2019 16:50
Vagrant File for Configuring Webservers
# -*- mode: ruby -*-
# vi: set ft=ruby :
# All Vagrant configuration is done below. The "2" in Vagrant.configure
# configures the configuration version (we support older styles for
# backwards compatibility). Please don't change it unless you know what
# you're doing.
Vagrant.configure("2") do |config|
# The most common configuration options are documented and commented below.
# For a complete reference, please see the online documentation at
@tbhaxor
tbhaxor / async_as_sync.html
Created May 6, 2019 16:55
Running async code synchronously
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta http-equiv="X-UA-Compatible" content="ie=edge" />
<title>Running Async code Synchronously</title>
</head>
<body>
<script>
@tbhaxor
tbhaxor / Linear Search.ipynb
Created May 9, 2019 20:49
Linear search explained
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tbhaxor / Selection Sort.ipynb
Created May 9, 2019 20:51
Selection sort explained
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tbhaxor / Introduction_CPP.ipynb
Created May 10, 2019 19:57
Introduction to C++
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tbhaxor / DataTypes and Variables in C++.ipynb
Created May 10, 2019 20:31
About datatypes and variables in c++
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tbhaxor / Compound Data Types.ipynb
Created May 11, 2019 15:13
User defined datatypes in C++
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tbhaxor / Control Statements.ipynb
Created May 11, 2019 16:16
C++ Control Structure
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tbhaxor / Eigenvector_and_Eigenvalues.md
Created May 13, 2019 20:21
Eigenvector and Eigenvalues crash course

Linear Transformation

  • Linear transformation of X with Y is X . Y
  • Transformation matrix is used to change direction, magnitude and sometimes both of the matrix

Eigenvector and Eigenvalue

  • As we look deeper into machine learning concepts, ability to transform a vector by only a factoring scalor is very useful
  • Eigenvectors, is a scalar multiple of itself when multiplied by transformation matrix
  • Eigenvalue, is the scalar multiple of eigenvector
  • The first step is to find eigenvalues
  • The vector only change in magnitude not in direction is represented by 1A . x̅ = λ . x̅, where A is the transformation matrix, is the eigenvector and λ is the eigenvalue