Machine Learning in R

Library for automatic retraining and continual learning

Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.

Python Scripts and Jupyter Notebooks

Hyperparameter, Make configurable AI applications.Build for Python hackers.

The accompanying repo for the hyperparameters optimization bdx meetup talk, blog post and webinar

Implementations of Genetic Methods for Financial Machine Learning Applications

Applying Population Based Training on Generative Adversarial Networks.

This repository contains the implementation of a recurrent neural network (LSTM from keras library) with the purpose of forecasting target time series, given the targets historical records and covariates. The project uses a toy data set, while focusing on the data transformation tasks (pandas dataframes to 3D numpy arrays required by recurrent n…

I have trained two different CNN models for binary image classification to see which architecture has better accuracy, takes less time in training, how hyperparamters affect training and how many epochs do each of them need. I achieved 96% accuracy on the best model.

Sentiment Analysis in texts written in French language using Tensorflow/Keras (and using XGBoost for hyperparameters optimization)

Supplementary material for DOLAP 2019 submission

a CMA-ES based hyperparameter optimization tool for NMT.

Diferentes processos que podem ser usados para encontrar os hyperparâmetros ótimos em aplicações de Inteligência Artificial.

An exploratory data analysis is performed and a regression model is used to predict house values. The prediction performance is optimized after tuning the model hyper-parameters to minimize bias/variance errors.

A library to build and run HyperOpt Hyper Parameter Optimization Schemes

Classification-Techniques-For-Fraud-Detection