[ECCV 2020 Spotlight] A Simple and Versatile Framework for Image-to-Image Translation
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Updated
Jul 25, 2024 - Python
[ECCV 2020 Spotlight] A Simple and Versatile Framework for Image-to-Image Translation
Open source machine learning library with various machine learning tools
Converting night into day is one of the most interesting applications in generative models, due to the great difficulty in recreating the scene during the day, especially in cases of extreme darkness, and thus the difficulty lies in imagining the scene during the day when the lighting is very weak.
This repository contains source code to the article: Piotr Szwed: Classification and feature transformation with Fuzzy Cognitive Maps, Applied Soft Computing, Elsevier 2021
Creating Customer Segments - 4th project for Udacity's Machine Learning Nanodegree
Feature engineering in machine learning
Code for <Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents>
TSIT implementation in TensorFlow; TSIT: A Simple and Versatile Framework for Image-to-Image Translation
Implementation of the stacked autoencoder in Tensorflow
Machine Learning Engineer Nanodegree, Unsupervised Learning, Creating Customer Segments
A collection of working snippets used for machine learning related tasks.
Extracting, transforming and selecting features using Spark MLlib
Airbnb price prediction with machine learning models using Amsterdam dataset.
Apply unsupervised learning techniques to identify customers segments.
Tahap 1 Tugas Besar - data preprocessing pada dataset Telco Customer Churn
This project aims to predict Prices of House. It involves several key stages, including data preprocessing, feature engineering, model selection, and evaluation. The goal is to develop a model that provides accurate and reliable price predictions based on the given features.
Customer Segments - Machine Learning Nanodegree from Udacity
Scikit-klearn compatible BinaryEncoder class capable of handling unseen categories in an automated fashion
Machine Learning Nano-degree Project : To identify customer segments hidden in product spending data collected for customers of a wholesale distributor
Using the dataset compiled by Dean De Cock. Applying Feature Transformation, Feature Selection and K-fold Cross Validation
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