Amazing-Python-Scripts - 🚀 Curated collection of Amazing Python scripts from Basics to Advance with automation task scripts.

Overview

Amazing-Python-Scripts

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📑 Introduction

A curated collection of Amazing Python scripts from Basics to Advance with automation task scripts. This is your Personal space to find or add any new script that can make Life Easier for you, as a Developer, and find a utility of coding to burst out of boredom. Get started with coding exhilarating scripts that you can use to download PDFs from an Online Source or just to randomly like everyone's Instagram Post.

📃 Scripts Available

👨🏻‍💻 How to get started?

You can refer to the following articles on basics of Git and Github and also contact the Project Mentors, in case you are stuck:

📝 How to Contribute?

  • Take a look at Contributing Guide
  • Take a look at the Existing Issues or create your own Issues!
  • Wait for the Issue to be assigned to you after which you can start working on it.
  • Fork the Repo and create a Branch for any Issue that you are working upon.
  • Create a Pull Request which will be promptly reviewed and suggestions would be added to improve it.
  • Add Screenshots to help us know what this Script is all about.

Contributors

Thanks go to these Wonderful People 👨🏻‍💻: 🚀 Contributions of any kind are welcome!

🌟 Stargazers Over Time 🌟

Stargazers over time

Project Maintainers ❤️


Avinash Ranjan


Kaustubh Gupta


Antriksh Misri


Santushti Sharma


Kushal Das

Happy Coding 👨‍💻

Owner
Avinash Ranjan
VP Tech @one24store • GDSC Lead @dscciem • Volunteer @TheOpenCode • MERN Stack & Flutter Developer | DevOps and Tech Blogging
Avinash Ranjan
Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis

Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis [Paper] [Online Demo] The following results are obtained by our SCUNet with purely syn

Kai Zhang 312 Jan 07, 2023
Benchmarks for Model-Based Optimization

Design-Bench Design-Bench is a benchmarking framework for solving automatic design problems that involve choosing an input that maximizes a black-box

Brandon Trabucco 43 Dec 20, 2022
DeepLabv3+:Encoder-Decoder with Atrous Separable Convolution语义分割模型在tensorflow2当中的实现

DeepLabv3+:Encoder-Decoder with Atrous Separable Convolution语义分割模型在tensorflow2当中的实现 目录 性能情况 Performance 所需环境 Environment 注意事项 Attention 文件下载 Download

Bubbliiiing 31 Nov 25, 2022
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

DistMIS Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation. DistriMIS Distributing Deep Learning Hyperparameter Tuning

HiEST 2 Sep 09, 2022
GAT - Graph Attention Network (PyTorch) 💻 + graphs + 📣 = ❤️

GAT - Graph Attention Network (PyTorch) 💻 + graphs + 📣 = ❤️ This repo contains a PyTorch implementation of the original GAT paper ( 🔗 Veličković et

Aleksa Gordić 1.9k Jan 09, 2023
The implemention of Video Depth Estimation by Fusing Flow-to-Depth Proposals

Flow-to-depth (FDNet) video-depth-estimation This is the implementation of paper Video Depth Estimation by Fusing Flow-to-Depth Proposals Jiaxin Xie,

32 Jun 14, 2022
Learned model to estimate number of distinct values (NDV) of a population using a small sample.

Learned NDV estimator Learned model to estimate number of distinct values (NDV) of a population using a small sample. The model approximates the maxim

2 Nov 21, 2022
Implementation of Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis

acLSTM_motion This folder contains an implementation of acRNN for the CMU motion database written in Pytorch. See the following links for more backgro

Yi_Zhou 61 Sep 07, 2022
Python framework for Stochastic Differential Equations modeling

SDElearn: a Python package for SDE modeling This package implements functionalities for working with Stochastic Differential Equations models (SDEs fo

4 May 10, 2022
Machine learning framework for both deep learning and traditional algorithms

NeoML is an end-to-end machine learning framework that allows you to build, train, and deploy ML models. This framework is used by ABBYY engineers for

NeoML 704 Dec 27, 2022
A wrapper around SageMaker ML Lineage Tracking extending ML Lineage to end-to-end ML lifecycles, including additional capabilities around Feature Store groups, queries, and other relevant artifacts.

ML Lineage Helper This library is a wrapper around the SageMaker SDK to support ease of lineage tracking across the ML lifecycle. Lineage artifacts in

AWS Samples 12 Nov 01, 2022
Build and run Docker containers leveraging NVIDIA GPUs

NVIDIA Container Toolkit Introduction The NVIDIA Container Toolkit allows users to build and run GPU accelerated Docker containers. The toolkit includ

NVIDIA Corporation 15.6k Jan 01, 2023
A Peer-to-peer Platform for Secure, Privacy-preserving, Decentralized Data Science

PyGrid is a peer-to-peer network of data owners and data scientists who can collectively train AI models using PySyft. PyGrid is also the central serv

OpenMined 615 Jan 03, 2023
Multi-Horizon-Forecasting-for-Limit-Order-Books

Multi-Horizon-Forecasting-for-Limit-Order-Books This jupyter notebook is used to demonstrate our work, Multi-Horizon Forecasting for Limit Order Books

Zihao Zhang 116 Dec 23, 2022
A PyTorch implementation of the paper "Semantic Image Synthesis via Adversarial Learning" in ICCV 2017

Semantic Image Synthesis via Adversarial Learning This is a PyTorch implementation of the paper Semantic Image Synthesis via Adversarial Learning. Req

Seonghyeon Nam 146 Nov 25, 2022
Website for D2C paper

D2C This is the repository that contains source code for the D2C Website. If you find D2C useful for your work please cite: @article{sinha2021d2c au

1 Oct 21, 2021
Code for paper "Vocabulary Learning via Optimal Transport for Neural Machine Translation"

**Codebase and data are uploaded in progress. ** VOLT(-py) is a vocabulary learning codebase that allows researchers and developers to automaticaly ge

416 Jan 09, 2023
Segmentation Training Pipeline

Segmentation Training Pipeline This package is a part of Musket ML framework. Reasons to use Segmentation Pipeline Segmentation Pipeline was developed

Musket ML 52 Dec 12, 2022
Implementation of Heterogeneous Graph Attention Network

HetGAN Implementation of Heterogeneous Graph Attention Network This is the code repository of paper "Prediction of Metro Ridership During the COVID-19

5 Dec 28, 2021
Code for "LASR: Learning Articulated Shape Reconstruction from a Monocular Video". CVPR 2021.

LASR Installation Build with conda conda env create -f lasr.yml conda activate lasr # install softras cd third_party/softras; python setup.py install;

Google 157 Dec 26, 2022