Rule Based Classification Project

Last update: Jan 12, 2022

Overview

Kural Tabanlı Sınıflandırma ile Potansiyel Müşteri Getirisi Hesaplama

İş Problemi: Bir oyun şirketi müşterilerinin bazı özelliklerini kullanaraknseviye tabanlı (level based) yeni müşteri tanımları (persona) oluşturmak ve bu yeni müşteri tanımlarına göre segmentler oluşturup bu segmentlere göre yeni gelebilecek müşterilerin şirkete ortalama ne kadar kazandırabileceğini tahmin etmek istemektedir.

Örneğin: Türkiye’den IOS kullanıcısı olan 25 yaşındaki bir erkek kullanıcının ortalama ne kadar kazandırabileceği belirlenmek isteniyor.

Owner

Şafak

GitHub Repository

This is the pytorch implementation for the paper: Learning Accurate Performance Predictors for Ultrafast Automated Model Compression, which is in submission to TPAMI

SeerNet This is the pytorch implementation for the paper: Learning Accurate Performance Predictors for Ultrafast Automated Model Compression, which is

3 May 01, 2022

Solution to the Weather4cast 2021 challenge

This code was used for the entry by the team "antfugue" for the Weather4cast 2021 Challenge. Below, you can find the instructions for generating predi

13 Jan 03, 2023

Natural Intelligence is still a pretty good idea.

Human Learn Machine Learning models should play by the rules, literally. Project Goal Back in the old days, it was common to write rule-based systems.

641 Dec 26, 2022

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

OCTIS : Optimizing and Comparing Topic Models is Simple! OCTIS (Optimizing and Comparing Topic models Is Simple) aims at training, analyzing and compa

478 Jan 01, 2023

ImageNet-CoG is a benchmark for concept generalization. It provides a full evaluation framework for pre-trained visual representations which measure how well they generalize to unseen concepts.

The ImageNet-CoG Benchmark Project Website Paper (arXiv) Code repository for the ImageNet-CoG Benchmark introduced in the paper "Concept Generalizatio

23 Oct 09, 2022

PyTorch implementation of 1712.06087 "Zero-Shot" Super-Resolution using Deep Internal Learning

Unofficial PyTorch implementation of "Zero-Shot" Super-Resolution using Deep Internal Learning Unofficial Implementation of 1712.06087 "Zero-Shot" Sup

196 Nov 27, 2022

Aesara is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

898 Jan 07, 2023

Rule Based Classification Project

Related tags

Overview

Kural Tabanlı Sınıflandırma ile Potansiyel Müşteri Getirisi Hesaplama

Owner

Şafak

This is the pytorch implementation for the paper: Learning Accurate Performance Predictors for Ultrafast Automated Model Compression, which is in submission to TPAMI

Solution to the Weather4cast 2021 challenge

Natural Intelligence is still a pretty good idea.

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

ImageNet-CoG is a benchmark for concept generalization. It provides a full evaluation framework for pre-trained visual representations which measure how well they generalize to unseen concepts.

PyTorch implementation of 1712.06087 "Zero-Shot" Super-Resolution using Deep Internal Learning

Aesara is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

PyTorch implementation of "Continual Learning with Deep Generative Replay", NIPS 2017

Code & Data for Enhancing Photorealism Enhancement

StableSims is an open-source project aimed at simulating MakerDAO's Dai stablecoin system

JAX-based neural network library

Bu repo SAHI uygulamasını mantığını öğreniyoruz.

Animal Sound Classification (Cats Vrs Dogs Audio Sentiment Classification)

Can we learn gradients by Hamiltonian Neural Networks?

Covid-19 Test AI (Deep Learning - NNs) Software. Accuracy is the %96.5, loss is the 0.09 :)

Contrastive Loss Gradient Attack (CLGA)

Pose Transformers: Human Motion Prediction with Non-Autoregressive Transformers

pytorch, hand(object) detect ,yolo v5，手检测

Learning Spatio-Temporal Transformer for Visual Tracking

A Python Library for Graph Outlier Detection (Anomaly Detection)

Rule Based Classification Project

Related tags

Overview

Kural Tabanlı Sınıflandırma ile Potansiyel Müşteri Getirisi Hesaplama

Owner

Şafak

This is the pytorch implementation for the paper: *Learning Accurate Performance Predictors for Ultrafast Automated Model Compression*, which is in submission to TPAMI

Solution to the Weather4cast 2021 challenge

Natural Intelligence is still a pretty good idea.

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

ImageNet-CoG is a benchmark for concept generalization. It provides a full evaluation framework for pre-trained visual representations which measure how well they generalize to unseen concepts.

PyTorch implementation of 1712.06087 "Zero-Shot" Super-Resolution using Deep Internal Learning

Aesara is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

PyTorch implementation of "Continual Learning with Deep Generative Replay", NIPS 2017

Code & Data for Enhancing Photorealism Enhancement

StableSims is an open-source project aimed at simulating MakerDAO's Dai stablecoin system

JAX-based neural network library

Bu repo SAHI uygulamasını mantığını öğreniyoruz.

Animal Sound Classification (Cats Vrs Dogs Audio Sentiment Classification)

Can we learn gradients by Hamiltonian Neural Networks?

Covid-19 Test AI (Deep Learning - NNs) Software. Accuracy is the %96.5, loss is the 0.09 :)

Contrastive Loss Gradient Attack (CLGA)

Pose Transformers: Human Motion Prediction with Non-Autoregressive Transformers

pytorch, hand(object) detect ,yolo v5，手检测

Learning Spatio-Temporal Transformer for Visual Tracking

A Python Library for Graph Outlier Detection (Anomaly Detection)

This is the pytorch implementation for the paper: Learning Accurate Performance Predictors for Ultrafast Automated Model Compression, which is in submission to TPAMI