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Artem Yankov yankov

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@karpathy
karpathy / min-char-rnn.py
Last active May 6, 2024 08:47
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
"""
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)
@jboner
jboner / latency.txt
Last active May 6, 2024 07:06
Latency Numbers Every Programmer Should Know
Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference 0.5 ns
Branch mispredict 5 ns
L2 cache reference 7 ns 14x L1 cache
Mutex lock/unlock 25 ns
Main memory reference 100 ns 20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy 3,000 ns 3 us
Send 1K bytes over 1 Gbps network 10,000 ns 10 us
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD
@ragingwind
ragingwind / Backend Architectures Keywords and References.md
Last active April 17, 2024 10:51
Backend Architectures Keywords and References
@debasishg
debasishg / gist:8172796
Last active March 15, 2024 15:05
A collection of links for streaming algorithms and data structures

General Background and Overview

  1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
  2. Models and Issues in Data Stream Systems
  3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
  4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
  5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t
@baraldilorenzo
baraldilorenzo / readme.md
Last active November 21, 2023 22:41
VGG-16 pre-trained model for Keras

##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

@postmodern
postmodern / rails_rce.rb
Last active July 17, 2023 11:54
Proof-of-Concept exploit for Rails Remote Code Execution (CVE-2013-0156)
#!/usr/bin/env ruby
#
# Proof-of-Concept exploit for Rails Remote Code Execution (CVE-2013-0156)
#
# ## Advisory
#
# https://groups.google.com/forum/#!topic/rubyonrails-security/61bkgvnSGTQ/discussion
#
# ## Caveats
#
@antirez
antirez / gist:4195130
Created December 3, 2012 13:44
<!DOCTYPE html>
<html>
<head><title>ChamberedTest</title></head>
<script type="text/javascript" src="js/chambered.js"></script>
<style type="text/css">
canvas, img {
image-rendering: optimizeSpeed;
image-rendering: -moz-crisp-edges;
image-rendering: -webkit-optimize-contrast;
@johnantoni
johnantoni / mysql.txt
Created August 7, 2012 18:57
mysql + vagrant + remote access
username: vagrant
password: vagrant
sudo apt-get update
sudo apt-get install build-essential zlib1g-dev git-core sqlite3 libsqlite3-dev
sudo aptitude install mysql-server mysql-client
sudo nano /etc/mysql/my.cnf
@carlosantoniodasilva
carlosantoniodasilva / post-receive
Created February 9, 2011 01:28
Basic git post-receive hook file to deploy a Rails app.
#!/bin/bash
APP_NAME="your-app-name-goes-here"
APP_PATH=/home/deploy/${APP_NAME}
# Production environment
export RAILS_ENV="production"
# This loads RVM into a shell session. Uncomment if you're using RVM system wide.
# [[ -s "/usr/local/lib/rvm" ]] && . "/usr/local/lib/rvm"
@pprett
pprett / grid_search.py
Created October 31, 2012 19:42
Parallel grid search for sklearn Gradient Boosting
"""Parallel grid search for sklearn's GradientBoosting.
This script uses IPython.parallel to run cross-validated
grid search on an IPython cluster. Each cell on the parameter grid
will be evaluated ``K`` times - results are stored in MongoDB.
The procedure tunes the number of trees ``n_estimators`` by averaging
the staged scores of the GBRT model averaged over all K folds.
You need an IPython ipcluster to connect to - for local use simply