lucindo Goto Github PK

blog

cabot-alert-pagerduty

dotfiles

my .emacs and related stuff

go-beacon

HTTP Beacon for boomerang.js and other analytics/trackers

goper

goper is a hoper clone. tracks urls redirects and so on.

graphpar

graph partitioning code from my master's thesis

keyper

simple key-value store

konig

simple distributed graph database (only a prototype)

krarup

Krarup message bus

lresolver

Simple local DNS resolver

lucindo.github.com

My github page

netutil

Golang network/http utilities

oldies

old and dummy code from pre-github era

r-trader

Mechanical Trading System using R (prototype for fun and learning purposes)

rstats

Running Statistics for Go

shttp

Simple zero-config HTTP Server

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

under

pet project to learn golang

video-transcoding-api

Agnostic API to transcode media assets across different cloud services.