Pinned Repositories
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
dhaaActiveLearning
dhaaDeepFeatures
divideae
LibOPF
LibOPF
dhaalves's Repositories
dhaalves/CEAL_keras
Implementation of "Cost-Effective Active Learning for Deep Image Classification" paper
dhaalves/dhaaActiveLearning
dhaalves/LibOPF
LibOPF
dhaalves/dhaaDeepFeatures
dhaalves/divideae
dhaalves/active-learning
dhaalves/architecture-decision-record
Architecture decision record (ADR) examples for software planning, IT leadership, and template documentation
dhaalves/awesome-design-patterns
A curated list of software and architecture related design patterns.
dhaalves/awesome-scalability
The Patterns of Scalable, Reliable, and Performant Large-Scale Systems
dhaalves/boticario-teste
dhaalves/compare_gan
Compare GAN code.
dhaalves/Controle-ponto
A chrome application for manage the hours of work
dhaalves/design-patterns-for-humans
An ultra-simplified explanation to design patterns
dhaalves/docklands
Minimalistic Java EE Related Dockerfiles
dhaalves/domain-driven-hexagon
Learn Domain-Driven Design, software architecture, design patterns, best practices. Code examples included
dhaalves/eap-build
Build scripts for EAP 6 or 7
dhaalves/heroku-buildpack-wildfly
dhaalves/ImageFilter
WEKA filter that uses LIRE to extract image features
dhaalves/LEA-53
dhaalves/MarketGAN
Implementing a Generative Adversarial Network on the Stock Market
dhaalves/modAL
A modular active learning framework for Python3
dhaalves/monkbananas
dhaalves/Pathfinding-Visualizer
A visualization tool for various pathfinding algorithms.
dhaalves/python-experiments
dhaalves/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.
dhaalves/test-github-pages
dhaalves/weka_experiments