dhaalves Goto Github PK

active-learning

architecture-decision-record

Architecture decision record (ADR) examples for software planning, IT leadership, and template documentation

awesome-design-patterns

A curated list of software and architecture related design patterns.

awesome-scalability

The Patterns of Scalable, Reliable, and Performant Large-Scale Systems

boticario-teste

ceal_keras

Implementation of "Cost-Effective Active Learning for Deep Image Classification" paper

compare_gan

Compare GAN code.

controle-ponto

A chrome application for manage the hours of work

design-patterns-for-humans

An ultra-simplified explanation to design patterns

dhaaactivelearning

dhaadeepfeatures

divideae

docklands

Minimalistic Java EE Related Dockerfiles

domain-driven-hexagon

Learn Domain-Driven Design, software architecture, design patterns, best practices. Code examples included

eap-build

Build scripts for EAP 6 or 7

heroku-buildpack-wildfly

imagefilter

WEKA filter that uses LIRE to extract image features

lea-53

libopf

LibOPF

marketgan

Implementing a Generative Adversarial Network on the Stock Market

modal

A modular active learning framework for Python3

monkbananas

pathfinding-visualizer

A visualization tool for various pathfinding algorithms.

python-experiments

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.

test-github-pages

weka_experiments