Find The Best Python Repositories For Your Project

Table of Contents Table of Contents Ghostbuster The problem Project Features Ins

NB Workflows Description If SQL is a lingua franca for querying data, Jupyter sh

DIGAN (ICLR 2022) Official PyTorch implementation of "Generating Videos with Dyn

Event Sourced Bank A "wide but shallow" example of using the Python event sourci

CrowdStrike Falcon Event Stream to Humio This project intend to provide a simple

google-drive-to-sqlite Create a SQLite database containing metadata from Google

📣 Welcome to the Rootski codebase! This is the codebase for the application run

autolfads-tf2 A TensorFlow 2.0 implementation of LFADS and AutoLFADS. Installati

MicBot MicBot uses Google Translate to speak everyone's chat messages. It can al

frbmclust Getting Started Clusterize FRB profiles using hierarchical clustering,

Kalman Filter Exercise What are Kalman Filters? A Kalman Filter is a algorithmic

DLWP: Deep Learning Weather Prediction DLWP is a Python project containing data-

Broksy's GIF Captioner Have you ever wanted to easily caption a GIF without havi

Gridlock Encryption Encryption and decryption of plain text messages inspired by

Udacity's CS101: Intro to Computer Science - Building a Search Engine All soluti

Torchlight 2 Lan Game Server Tool A message forwarding tool for Torchlight 2 lan

prompts read a textfile of prompts and import into anki via ankiconnect Usage In

A Network-Based High-Level Data Classification Algorithm Using Betweenness Centr

✨ Alpha Zero Bot ✨ Telegram Group Manager Bot + Userbot Written In Python Using

Raphtory Client This is the python client for the Raphtory project Install via p

SkyScrapers Collection of variety of Web Scraping Apps The web-scrapers involved

Tools Автоматический установщик хакерских утилит (ТОЛЬКО ДЛЯ ТЕРМУКС!) Оригиналь

Ontario-Covid19-Screening An automated Covid-19 School Screening Tool for Ontari

Youtube-downloader-using-Python Hii guys !! Fancy to see here Welcome! built by

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.