Pinned Repositories
gpt-summarizer
A scrappy Jupyter notebook to summarize long podcasts, youtube videos, etc
meli2020
9th (public) place solution to MeLi Data Challenge 2020
notebooks_tutoriais
Aqui você encontrar notebooks para alguns vídeos do meu canal no Youtube
swing
Implementation of the Swing Algorithm for Substitute Product Recommendation in Python
TimeSeriesForecasting
Material for the Time Series Forecasting article
TutorialEnsemble
Arquivos para o tutorial do artigo Tutorial: Aumentando o Poder Preditivo de Seus Modelos de Machine Learning com Stacking Ensembles
TutorialTitanic
tw-bert
Implementation of End-to-End Query Term Weighting (TW-BERT)
unified-embeddings
Implementation of Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
ledmaster's Repositories
ledmaster/notebooks_tutoriais
Aqui você encontrar notebooks para alguns vídeos do meu canal no Youtube
ledmaster/TimeSeriesForecasting
Material for the Time Series Forecasting article
ledmaster/TutorialTitanic
ledmaster/tw-bert
Implementation of End-to-End Query Term Weighting (TW-BERT)
ledmaster/gpt-summarizer
A scrappy Jupyter notebook to summarize long podcasts, youtube videos, etc
ledmaster/TutorialEnsemble
Arquivos para o tutorial do artigo Tutorial: Aumentando o Poder Preditivo de Seus Modelos de Machine Learning com Stacking Ensembles
ledmaster/meli2020
9th (public) place solution to MeLi Data Challenge 2020
ledmaster/learningcandlesticks
http://mariofilho.com/can-machine-learning-model-predict-the-sp500-by-looking-at-candlesticks/
ledmaster/english_tutorials
ledmaster/arxiv-sanidade
ArXiv Sanidade é uma aplicação web que ajuda os usuários a descobrir e salvar artigos relevantes do arXiv usando machine learning. Ele usa um modelo linear simples treinado nos artigos que o usuário salva explicitamente para prever a relevância de novos artigos.
ledmaster/unified-embeddings
Implementation of Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
ledmaster/recomendacaomachinelearning
Material do artigo: Como Criar um Sistema de Recomendação de Produtos Usando Machine Learning
ledmaster/clippy-adagrad
PyTorch Implementation of Improving Training Stability for Multitask Ranking Models in Recommender Systems
ledmaster/machinelearninginadimplencia
Material do artigo: Será Que Seu Cliente Vai Te Pagar? Usando Machine Learning Para Prever Inadimplência
ledmaster/multiple_steps_neural_network
How To Use Neural Networks to Forecast Multiple Steps of a Time Series
ledmaster/swing
Implementation of the Swing Algorithm for Substitute Product Recommendation in Python
ledmaster/machine-learning-success
How did you successfully apply machine learning in a company? Here we share the impact that deployed machine learning systems made on business related metrics.
ledmaster/AvitoSolution
Solução para a competição da Avito no Kaggle
ledmaster/Kaggle_CrowdFlower
1st Place Solution for Search Results Relevance Competition on Kaggle (https://www.kaggle.com/c/crowdflower-search-relevance)
ledmaster/TSPBrasil
Material sobre o artigo "Usando Otimização Para Aproximar a Menor Rota Entre Mais de 5.500 Municípios Brasileiros" publicado no site MarioFilho.com
ledmaster/xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow
ledmaster/AvazuSolution
Material do artigo sobre a competição Avazu
ledmaster/ec2SpotPrices
Uses boto to retrieve current spot instance prices on Amazon EC2.
ledmaster/fixes
ledmaster/ledmaster
ledmaster/multiple_steps_neural
ledmaster/nolearn
scikit-learn compatible wrappers for neural net libraries, and other utilities.
ledmaster/numerapi
Python API and command line interface for the numer.ai machine learning competition
ledmaster/OnlineSVMPegasos
Code and dataset for the article on implementation of Online SVM using Pegasos
ledmaster/Robyn
Robyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) package from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression, multi-objective evolutionary algorithm for hyperparameter optimisation, gradient-based optimisation for budget allocation etc.) to define media channel efficiency a