Find The Best Python Repositories For Your Project

水泽-信息收集自动化工具 郑重声明：文中所涉及的技术、思路和工具仅供以安全为目的的学习交流使用，任何人不得将其用于非法用途以及盈利等目的，否则后果自行承担。 0x01 介绍 作者：Ske 团队：0x727，未来一段时间将陆续开源工具，地址：https://github.com/0x727 定位：协助

Inductive entity representations from text via link prediction This repository contains the code used for the experiments in the paper "Inductive enti

Motion Representations for Articulated Animation This repository contains the source code for the CVPR'2021 paper Motion Representations for Articulat

GPT-Code-Clippy (GPT-CC) is an open source version of GitHub Copilot, a language model -- based on GPT-3, called GPT-Codex -- that is fine-tuned on publicly available code from GitHub.

LOC-FLOW is an “hands-free” earthquake location workflow to process continuous seismic records: from raw waveforms to well located earthquakes with magnitude calculations. The package assembles sever

Official Matplotlib cheat sheets

pytorch-a2c-ppo-acktr Update (April 12th, 2021) PPO is great, but Soft Actor Critic can be better for many continuous control tasks. Please check out

One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of

PyTorch SRResNet Implementation of Paper: "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"(https://arxiv.org/abs

FLAML - Fast and Lightweight AutoML

amogus.py - Version 0.0.5 amogus - Apple Music Hi-Res Artwork Fetcher this is my first real python tool so sorry if its bad amogus is a Python script

Official Matplotlib cheat sheets

The base to start an openapi project featuring: SQLModel, Typer, FastAPI, JWT Token Auth, Interactive Shell, Management Commands.

django-organizations Summary Groups and multi-user account management Author Ben Lopatin (http://benlopatin.com) Status Separate individual user ident

RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching This repository contains the source code for our paper: RAFT-Stereo: Multilevel

The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering"

Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation, available for both PyTorch and Tensorflow.

This is an official pytorch implementation of ActionCLIP: A New Paradigm for Video Action Recognition [arXiv] Overview Content Prerequisites Data Prep

functorch is a prototype of JAX-like composable function transforms for PyTorch.

Semi-supervised Domain Adaptation via Minimax Entropy (ICCV 2019) Install pip install -r requirements.txt The code is written for Pytorch 0.4.0, but s

Website | ArXiv | Get Start | Video PIRenderer The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic

This is an official pytorch implementation of ActionCLIP: A New Paradigm for Video Action Recognition [arXiv] Overview Content Prerequisites Data Prep

Differentiable Architecture Search Code accompanying the paper DARTS: Differentiable Architecture Search Hanxiao Liu, Karen Simonyan, Yiming Yang. arX

A comprehensive repository containing 30+ notebooks on learning machine learning!

PyTorch Mobile provides a runtime environment to execute state-of-the-art machine learning models on mobile devices. Latency is reduced, privacy preserved, and models can run on mobile devices anytime, anywhere.

In this blog post, we provide a quick overview of 10 currently available PyTorch Mobile powered demo apps running various state-of-the-art PyTorch 1.9 machine learning models spanning images, video, audio and text.

It’s never been easier to deploy a state-of-the-art ML model to a phone. You don’t need any domain knowledge in Machine Learning and we hope one of the below examples resonates enough with you to be the starting point for your next project.

Python Quick & Easy

This notebook contains an excerpt from the Python Programming and Numerical Methods - A Guide for Engineers and Scientists, the content is also available at Berkeley Python Numerical Methods.

Image segmentation model trained from scratch on the Oxford Pets dataset.

This example shows how to do image classification from scratch, starting from JPEG image files on disk, without leveraging pre-trained weights or a pre-made Keras Application model. We demonstrate the workflow on the Kaggle Cats vs Dogs binary classification dataset.

We use the image_dataset_from_directory utility to generate the datasets, and we use Keras image preprocessing layers for image standardization and data augmentation.

This example demonstrates how to implement a deep convolutional autoencoder for image denoising, mapping noisy digits images from the MNIST dataset to clean digits images. This implementation is based on an original blog post titled Building Autoencoders in Keras by François Chollet.

Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.

Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly, procedural), object-oriented, and functional programming. Python is often described as a "batteries included" language due to its comprehensive standard library.

As of December 2020 Python ranked third in TIOBE’s index of most popular programming languages, behind C and Java.

This example demonstrates two techniques for building memory-efficient recommendation models by reducing the size of the embedding tables, without sacrificing model effectiveness:

1. Quotient-remainder trick, by Hao-Jun Michael Shi et al., which reduces the number of embedding vectors to store, yet produces unique embedding vector for each item without explicit definition.
2. Mixed Dimension embeddings, by Antonio Ginart et al., which stores embedding vectors with mixed dimensions, where less popular items have reduced dimension embeddings.

An exploration of threads, processes, and coroutines in Python, with interesting examples that illuminate the differences between each.

Notice: This map is not a precise reflection of the state of the AI field, but just my subjective representation.

This is my first map as of the end of 2020 and will be extended in the future. It contains more than 200 words or phrases, so to describe all of them would be too extensive and overkill. Much more interesting (and useful for me) to tell how this map was gradually building in my head. I will not explain everything, just the main things, so it is normal not to understand something.

 Python 3.7.10 and 3.6.13,  the lastest security fix rollups for Python 3.7 and Python 3.6, are now available. You can find the release files, links to the changelogs, and more information here:

Learn how to build a robust and developer-friendly Python microservices infrastructure using gRPC and Kubernetes. You’ll also explore advanced topics such as interceptors and integration testing.