Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.
![Screenshot 2023-12-18 at 10 40 27 PM](https://private-user-images.githubusercontent.com/3837836/291468646-4c30ad72-76ee-4939-a5fb-16b570d38cf2.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.IfotNP5Z15K6UJlaH0OGw0bSWYbGDcDF15gfhLQ2ZTE)
Welcome to Jordan's grab-bag of common Binary Ninja Snippets. | |
These snippest are meant to run with the Binary Ninja Snippets Plugin | |
(http://github.com/Vector35/snippets) though they can all also be pasted | |
directly into the python console or turned into stand-alone plugins if needed. | |
To install the entire collection at once, just install the Snippets plugin via | |
the plugin manager (CMD/CTL-SHIFT-M), confirm the Snippet Editor works | |
(Tool/Snippets/Snippet Editor), and unzip this bundle (Download ZIP above) into | |
your Snippets folder. |
The following are appendices from Optics By Example, a comprehensive guide to optics from beginner to advanced! If you like the content below, there's plenty more where that came from; pick up the book!
import numpy as np | |
import tensorflow as tf | |
input_tensor = tf.constant(1, dtype=tf.int64) | |
keys = tf.constant(np.array([1,2,3]), dtype=tf.int64) | |
values = tf.constant(np.array([4,5,6]), dtype=tf.int64) | |
default_value = tf.constant(-1, dtype=tf.int64) | |
table = tf.contrib.lookup.HashTable( |
/* | |
Copyright (c) 2011, Thomas Dullien | |
All rights reserved. | |
Redistribution and use in source and binary forms, with or without | |
modification, are permitted provided that the following conditions | |
are met: | |
Redistributions of source code must retain the above copyright notice, | |
this list of conditions and the following disclaimer. Redistributions |
#!/usr/bin/env python | |
# Copyright 2017 Ryan Stortz (@withzombies) | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# |
(ns stats) | |
(defn mode [vs] | |
(let [fs (frequencies vs)] | |
(first (last (sort-by second fs))))) | |
(defn quantile | |
([p vs] | |
(let [svs (sort vs)] | |
(quantile p (count vs) svs (first svs) (last svs)))) |
#!/usr/bin/env sh | |
## | |
# This is script with usefull tips taken from: | |
# https://github.com/mathiasbynens/dotfiles/blob/master/.osx | |
# | |
# install it: | |
# curl -sL https://raw.github.com/gist/2108403/hack.sh | sh | |
# |
% -------------------------------------------------------------- | |
% This is all preamble stuff that you don't have to worry about. | |
% Head down to where it says "Start here" | |
% -------------------------------------------------------------- | |
\documentclass[12pt]{article} | |
\usepackage[margin=1in]{geometry} | |
\usepackage{amsmath,amsthm,amssymb} | |