Find The Best Python Repositories For Your Project

ICON: Implicit Clothed humans Obtained from Normals Yuliang Xiu · Jinlong Yang · Dimitrios Tzionas · Michael J. Black CVPR 2022 News 🚩 [2022/04/26] H

The Hands-on Reinforcement Learning course 🚀 From zero to HERO 🦸🏻‍🦸🏽 Out of intense complexities, intense simplicities emerge. -- Winston Churchi

F8Net Fixed-Point 8-bit Only Multiplication for Network Quantization (ICLR 2022 Oral) OpenReview | arXiv | PDF | Model Zoo | BibTex PyTorch implementa

BIG.art: Using Machine Learning to Create High-Res Fine Art How to use GLIDE and BSRGAN to create ultra-high-resolution paintings with fine details By

Bombcrypto Bot [Family JOW] Bot desenvolvido em python, 100% do código é aberto, para aqueles que tenham conhecimento validarem que não existe nenhum

Azure MLOps (v2) solution accelerator Welcome to the MLOps (v2) solution accelerator repository! This project is intended to serve as the starting poi

pybdshadow Introduction pybdshadow is a python package for generating, analyzing and visualizing building shadows from large scale building geographic

STATS305C: Applied Statistics III Instructor: Scott Linderman TA: Matt MacKay, James Yang Term: Spring 2022 Stanford University Course Description: Pr

RaVAEn The RaVÆn system We introduce the RaVÆn system, a lightweight, unsupervised approach for change detection in satellite data based on Variationa

mipnerf_pl Unofficial pytorch-lightning implement of Mip-NeRF, Here are some results generated by this repository (pre-trained models are provided bel

FantasyBert English | 中文 Introduction An easy-to-use framework for BERT models, with trainers, various NLP tasks and detailed annonations. You can imp

Fiddle Fiddle is a Python-first configuration library particularly well suited to ML applications. Fiddle enables deep configurability of parameters i

KARMA DDoS DDoS Script (DDoS Panel) with Multiple Bypass ( Cloudflare UAM,CAPTCHA,BFM,NOSEC / DDoS Guard / Google Shield / V Shield / Amazon / etc.. )

OcclusionFusion (CVPR'2022) Project Page | Paper | Video Overview This repository contains the code for the CVPR 2022 paper OcclusionFusion, where we

ViGA: Video moment retrieval via Glance Annotation This is the official repository of the paper "Video Moment Retrieval from Text Queries via Single F

DiscoStats cool statistics generated using your discord data. How? DiscoStats is not a service that breaks the Discord Terms of Service or Community G

HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation by Lukas Hoyer, Dengxin Dai, and Luc Van Gool [Arxiv] [Paper] Overview Unsup

A Human-in-the-Loop? workflow for creating HD images from text DALL·E Flow is an interactive workflow for generating high-definition images from text

Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis Abstract: This work targets at using a general deep lea

Introduction This codebase contains source-code of the Python-based implementation (ARES) of our SIGIR 2022 paper. Chen, Jia, et al. "Axiomatically Re

🔍 Watermarking Images in Self-Supervised Latent-Spaces PyTorch implementation and pretrained models for the paper. For details, see Watermarking Imag

Description 🖼️ Background 👴🏼 Vision 📜 Concepts 💬 Solutions' Lifecycle. Operations ⭕ Functionalities 🚀 Supported Cybersecurity Solutions 📦 Insta

LightHuBERT LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT | Github | Huggingface | SUPER

SelfRemaster: Self-Supervised Speech Restoration Official implementation of SelfRemaster: Self-Supervised Speech Restoration with Analysis-by-Synthesi

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.