semvec/pypi-packages huggingface dataset:
redrawing 1.0.3
Python package provinding tools for artistic interactive applications using AI
License: MIT License
Requires Python: >=3.6

[Documentation](https://redrawing.github.io/docs/)

# redrawing

Python package provinding tools for artistic interactive applications using AI

Created by ReDrawing Campinas team for the OpenCV AI 2021 Competition.


## Installation

```
python -m pip install --upgrade pip
python -m pip install redrawing
```

## Stages

Stages are the basic processing units of the redrawing package. They receive data objects from input channels, processes them and output data in the output channels.

Examples of stages:

Stage name | Use
--- | ---
OAK_Stage | Handles a OAK camera. Can be changed using OAK_Substages, like a Body detector or a Hand gesture detector
PCR_Viewer | Visualizer for image, depth and body pose data
CameraReceiver/IMUReceiver | Receives data from a smarthphone
UKF_IMU | UKF filter for orientation estimation using IMU data

## Data and communication

All inter stage data communication occours using Data classes.

Examples of data classes:

Data class | Use
--- | ---
BodyPose | Stores body pose data (keypoints)
Image | Stores image data
Depth_Map | Stores depth data
IMU | Stores IMU data

For exchange data with client applications, the UDP_Stage can be used, using UDP protocol with JSON converted messages. We also have client libraries for receiving and decoding data:

- [redrawing_java](https://github.com/ReDrawing/redrawing_java): client library for Java
- ReDrawing for Processing: client library for Processing, encapsulates the redrawing_java library

# Authors
- [Artemis Sanchez Moroni](https://github.com/ArtemisMoroni)
- [Cássio Gião Dezzotti](https://github.com/cassiodezotti)
- [Daniele Souza Gonçalves](https://github.com/danielegsouza)
- [Elton Cardoso do Nascimento](https://github.com/EltonCN)
- [Gabriel Tadao Kuae](https://github.com/kuta-ga)
- [Jônatas Manzolli]()
- [Marcela Medicina](https://github.com/mmedicina)
- [Pedro Victor Vieira de Paiva](https://github.com/enemy537)
- [Tiago Fernandes Tavares](https://github.com/tiagoft)
- [Thiago Danilo Silva de Lacerda](https://github.com/ThiagoDSL)

## Legal
Copyright (c) 2021 ReDrawing Campinas AUTHORS

Submodules in ai_models/third_models submodules may be licensed under other licenses. A copy of the respective license can be found in the submodule folder.

Made with the support of "Conselho Nacional de Desenvolvimento Científico e Tecnológico" (CNPq) of Brazil
easyrec-python 0.0.2
Easy-to-use implementations of well-known recommender system algorithms based on Python Tensorflow 2.
License: MIT License
Keywords: Recommender System; CTR Estimation

## Introduction

*easyrec* is an open-sourced and easy-to-use recommender system toolbox based on tensorflow 2.

## License

This project is released under the [MIT License](https://github.com/xu-zhiwei/easyrec/blob/main/LICENSE).

## Features

| model | source |
| ---- | ---- |
| Logisitic Regression (LR) | |
| Multi-layer Perceptron (MLP) | |
| Factorization Machine (FM) | Steffen Rendle. Factorization Machines. ICDM. 2010. |
| Deep Structured Semantic Model (DSSM) | Po-Sen Huang et al. Learning Deep Structured Semantic Models for Web Search using Clickthrough Data. CIKM. 2013. |
| Field-aware Factorization Machine (FFM) | Yuchin Juan et al. Field-aware Factorization Machines for CTR Prediction. RecSys. 2016. |
| Deep Crossing | Ying Shan et al. Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features. KDD. 2016. |
| Factorization-machine supported Neural Network (FNN) | Weinan Zhang. Deep Learning over Multi-field Categorical Data – A Case Study on User Response Prediction. ECIR. 2016. |
| Product-based Neural Network (PNN) | Yanru Qu et al. Product-based Neural Networks for User Response Prediction. ICDM. 2016. |
| Wide & Deep | Heng-Tze Cheng et al. Wide & Deep Learning for Recommender Systems. RecSys. 2016. |
| Attentional Factorization Machine (AFM) | Jun Xiao et al. Attentional Factorization Machines:Learning the Weight of Feature Interactions via Attention Networks. arXiv. 2017. |
| Neural Factorization Machine (NFM) | Xiangnan He et al. Neural Factorization Machines for Sparse Predictive Analytics. SIGIR. 2017. |
| Neural Matrix Factorization (NMF) | Xiangnan He et al. Neural Collaborative Filtering. WWW. 2017. |
| Deep & Cross Network (DCN) | Ruoxi Wang et al. Deep & Cross Network for ad Click Predictions. ADKDD. 2017. |
| Deep Factorization Machine (DeepFM) | Huifeng Guo et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. arXiv. 2017. |
| Extreme Deep Factorization Machine (xDeepFM) | Jianxun Lian et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems. KDD. 2018. |
| Multi-gate Mixture-of-Experts (MMOE) | Jiaqi Ma et al. Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts. KDD. 2018. |
| Automatic Feature Interaction (AutoInt) | AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks. CIKM. 2019. |

## Installation

```
pip install easyrec-python
```

## Getting Started

Please refer to the [documentation](https://easyrec-python.readthedocs.io/en/latest/) for the basic usage of *easyrec*.