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👉Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
😎TOPICS: programming,development,design,design-system,system,design-patterns,web,web-application,webapp,python,interview,interview-questions,interview-practice
⭐️STARS：108035, 今日上升数↑:152
👉:house_with_garden: Open source home automation that puts local control and privacy first
😎TOPICS: python,home-automation,iot,internet-of-things,mqtt,raspberry-pi,asyncio
⭐️STARS：35859, 今日上升数↑:187
👉README：
Home Assistant |Chat Status|
Open source home automation that puts local control and privacy first. Powered by a worldwide community of tinkerers and DIY enthusiasts. Perfect to run on a Raspberry Pi or a local server.
Check out home-assistant.io <https://home-assistant.io>__ for a demo <https://home-assistant.io/demo/>, installation instructions <https://home-assistant.io/getting-started/>, tutorials <https://home-assistant.io/getting-started/automation-2/>__ and documentation <https://home-assistant.io/docs/>__.
|screenshot-states|
Featured integrations
|screenshot-components|
The system is built using a modular approach so support for other devices or actions can be implemented easily. See also the `section on architecture <htt...
👉Code for the paper "Jukebox: A Generative Model for Music"
😎TOPICS: paper,audio,music,pytorch,generative-model,vq-vae,transformer
⭐️STARS：3339, 今日上升数↑:15
👉README：
Status: Archive (code is provided as-is, no updates expected)
👉A more or less universal SSL unpinning tool for iOS
😎TOPICS: ios,ssl-pinning,bypass,certificate-pinning,frida
⭐️STARS：122, 今日上升数↑:20
👉README：
MEDUZA
"MEDUZA" ("медуза") means "jellyfish" in Ukrainian 🇺🇦.
What is MEDUZA?
It's a Frida-based tool, my replacement for SSLKillSwitch. I created it for in-house use, but then decided to opensource it. TBH, I hate open source, but the world is full of compromises... :(
How does it work?
It's simple. First time, you run an app without sniffing and use it as usual. MEDUZA is sitting quietly and collecting certificates used by the app to connect servers. Then MEDUZA generates a Frida script that fakes (==upnin) the collected certificates. So you run the app for second time, use the generated script, and catch the traffic with mitmproxy.
Limitations
MEDUZA can only unpin apps using iOS system SSL libs. Some apps (e.g. Instagram) do not use the system SSL libs, they implement some third-party custom SSL stack (for example, Instagram uses OpenSSL statically linked to an Instagram private frameworks, see [InstagramSSLP...
👉:art: Diagram as Code for prototyping cloud system architectures
😎TOPICS: diagram,diagram-as-code,drawing,architecture,prototyping
⭐️STARS：9170, 今日上升数↑:352
👉README：
Diagrams
Diagram as Code.
Diagrams lets you draw the cloud system architecture in Python code. It was born for prototyping a new system architecture design without any design tools. You can also describe or visualize the existing system architecture as well. Diagrams currently supports main major providers including: AWS, Azure, GCP, Kubernetes, Alibaba Cloud, Oracle Cloud etc... It also supports On-Premise nodes, SaaS and major Programming frameworks and languages.
Diagram as Code also allows you to track the architecture diagram changes in any version control system.
NOTE: It does not control any actual cloud resources nor does it generate cloud formation or terra...
👉The Web framework for perfectionists with deadlines.
😎TOPICS: python,django,web,framework,orm,templates,models,views,apps
⭐️STARS：52467, 今日上升数↑:58
👉README：
======
Django
Django is a high-level Python Web framework that encourages rapid development
and clean, pragmatic design. Thanks for checking it out.
All documentation is in the "docs" directory and online at https://docs.djangoproject.com/en/stable/. If you're just getting started,
here's how we recommend you read the docs:
First, read docs/intro/install.txt for instructions on installing Django.
Next, work through the tutorials in order (docs/intro/tutorial01.txt, docs/intro/tutorial02.txt, etc.).
If you want to set up an actual deployment server, read docs/howto/deployment/index.txt for instructions.
You'll probably want to read through the topical guides (in docs/topics)
next; from there you can jump to the HOWTOs (in docs/howto) for specific
problems, and check out the reference (docs/ref) for gory details.
See docs/README for instructions on building an HTML version of the docs.
Docs are updated rigorously. If you find any problems in t...
For information on contributing to this project, please see the contributing guide.
Please note a passing build status indicates all listed APIs are available since the last update. A failing build status indicates that 1 or more services may be unavailable at the moment.
👉A cross-platform network media aggregation application that supports online viewing or listening of live video, HD TV and radio stations. 一个跨平台的网络媒体聚合应用，支持直播视频，高清电视和广播电台的在线观看或收听。
😎TOPICS: cross-platform,media,livestream,live,live-video,hdtv,radio-station
⭐️STARS：520, 今日上升数↑:49
👉DeDRM tools for ebooks
😎TOPICS: ``
⭐️STARS：8387, 今日上升数↑:62
👉README：
DeDRM_tools
DeDRM tools for ebooks
This is a repository of all the scripts and other tools for removing DRM from ebooks that I could find, committed in date order as best as I could manage. (Except for the Requiem tools for Apple's iBooks, and Convert LIT for Microsoft's .lit ebooks.)
Mostly it tracks the tools released by Apprentice Alf, athough it also includes the individual tools and their histories from before Alf had a blog.
Users should download the latest zip archive.
Developers might be interested in forking the repository, as it contains unzipped versions of those tools that are zipped to make the changes over time easier to follow.
For the latest Amazon KFX format, users of the calibre plugin should also install the KFX Input pl...
👉A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
😎TOPICS: risk,pricing,risk-management,asset-allocation,finance,valuation,python,derivatives-pricing,numba,bonds,students,fixed-income,derivatives,investment,currency,credit
⭐️STARS：86, 今日上升数↑:30
👉README：
FinancePy
FinancePy is a python-based library that is currently in beta version. It covers the following functionality:
Valuation and risk models for a wide range of equity, FX, interest rate and credit derivatives.
Although it is written entirely in Python, it can achieve speeds comparable to C++ by using Numba. As a result the user has both the ability to examine the underlying code and the ability to perform pricing and risk at speeds which compare to a library written in C++.
The target audience for this library includes:
Students wishing to learn derivative pricing and Python.
Professors wishing to teach derivative pricing and Python.
Traders wishing to price or risk-manage a derivative.
Quantitative analysts seeking to price or reverse engineer a price.
Risk managers wishing to replicate and understand a price.
Portfolio managers wishing to check prices or calculate risk measures
Fund managers wanting to value a portfolio or examine a trading strategy
👉⚡ Automatically decrypt encryptions without knowing the key or cipher, decode encodings, and crack hashes ⚡
😎TOPICS: decryption,natural-language-processing,cryptography,cipher,artificial-intelligence,ctf-tools,ctf,cpp,python,hacking,pentesting,deep-neural-network,hashes,cyberchef-magic,encryptions,encodings
⭐️STARS：4588, 今日上升数↑:35
👉PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.
😎TOPICS: ``
⭐️STARS：2960, 今日上升数↑:7
👉README：
PySlowFast
PySlowFast is an open source video understanding codebase from FAIR that provides state-of-the-art video classification models with efficient training. This repository includes implementations of the following methods:
The goal of PySlowFast is to provide a high-performance, light-weight pytorch codebase provides state-of-the-art video backbones for video understanding research on different tasks (classification, detection, and etc). It is designed in order to support rapid implementation and evaluation of novel video research ideas. PySlowFast includes implementatio...
This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. All code and models are under active development, and are subject to modification or deletion without notice. Use at your own risk.
** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. EfficientDet data from [google/automl](https://github.co...
👉Analytical Web Apps for Python, R, Julia, and Jupyter. No JavaScript Required.
😎TOPICS: dash,plotly,data-visualization,data-science,gui-framework,flask,react,python,finance,bioinformatics,technical-computing,charting,plotly-dash,web-app,productivity,modeling,r,rstats,jupyter,julia
⭐️STARS：12966, 今日上升数↑:14
👉README：
Dash
Dash is a Python framework for building analytical web applications. No JavaScript required.
Built on top of Plotly.js, React and Flask, Dash ties modern UI elements like dropdowns, sliders, and graphs directly to your analytical Python code. Read our tutorial proudly crafted ❤️ by Dash itself.
👉Sentry is cross-platform application monitoring, with a focus on error reporting.
😎TOPICS: crash-reporting,crash-reports,error-monitoring,monitoring,devops,csp-report,django,error-logging
⭐️STARS：26183, 今日上升数↑:11
👉README：
Users and logs provide clues. Sentry provides answers.
What's Sentry?
Sentry is a service that helps you monitor and fix crashes
in realtime. The server is in Python, but it contains a full API for
sending events from any language, in any application.
👉A modular, high performance, headless e-commerce storefront built with Python, GraphQL, Django, and ReactJS.
😎TOPICS: python,e-commerce,django,storefront,store,commerce,shop,ecommerce-storefront,ecommerce,cart,ecommerce-platform,react,pwa,graphql,headless,headless-ecommerce,headless-commerce
⭐️STARS：9160, 今日上升数↑:16
👉README：
Saleor Commerce
Customer-centric e-commerce on a modern stack
A headless, GraphQL-first e-commerce platform delivering ultra-fast, dynamic, personalized shopping experiences. Beautiful online stores, anywhere, on any device.
👉Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time
😎TOPICS: 3d-luts,photo-retouching,image-enhancement,color-enhancement,color-manipulation,computational-photography,image-processing
⭐️STARS：127, 今日上升数↑:26
👉README：
Image-Adaptive-3DLUT
Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time
The whole datasets used in the paper are over 300G. Here I only provided the FiveK dataset resized into 480p resolution (including 8-bit sRGB, 16-bit XYZ inputs and 8-bit sRGB targets). I also provided 10 full-resolution images for testing speed. To obtain the entire full-resolution images, it is recommended to transform from the original FiveK dataset.
A model trained on the 480p resolution can be directly applied to images of 4K (or higher) resolution without performance drop. This can significantly speedup the trainin...
👉Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
😎TOPICS: ``
⭐️STARS：18439, 今日上升数↑:33
👉README：
Mask R-CNN for Object Detection and Segmentation
This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.
The repository includes:
Source code of Mask R-CNN built on FPN and ResNet101.
Training code for MS COCO
Pre-trained weights for MS COCO
Jupyter notebooks to visualize the detection pipeline at every step
ParallelModel class for multi-GPU training
Evaluation on MS COCO metrics (AP)
Example of training on your own dataset
The code is documented and designed to be easy to extend. If you use it in your research, please consider citing this repository (bibtex below). If you work on 3D vision, you might find our recently released Matterport3D dataset useful as well.
This dataset was ...
This repo contains all my work for this specialization. All the code base, quiz questions, screenshot, and images, are taken from, unless specified, Deep Learning Specialization on Coursera.
What I want to say
VERBOSE CONTENT WARNING: YOU CAN JUMP TO THE NEXT SECTION IF YOU WANT
As a CS major student and a long-time self-taught learner, I have completed many CS related MOOCs on Coursera, Udacity, Udemy, and Edx. I do understand the hard time you spend on understanding new concepts and debugging your program. There are discussion forums on most MOOC platforms, however, even a question with detailed description may need some time to be answered. Here I released these solutions, which are only for your reference purpose. It may help you to save some time. And I hope you don't copy any pa...
👉A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
😎TOPICS: ``
⭐️STARS：10415, 今日上升数↑:16
Note: If you are looking for the first edition notebooks, check out ageron/handson-ml.
Quick Start
Want to play with these notebooks online without having to install anything?
Use any of the following services.
WARNING: Please be aware that these services provide temporary environments: anything you do will be deleted after a while, so make sure you download any data you care about.
Recommended: open this repository in [Colaboratory](http...
👉Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker
😎TOPICS: amazon,sagemaker,example,notebooks,machine,deep,learning,aws,rl,reinforcement-learning
⭐️STARS：4271, 今日上升数↑:6
👉README：
Amazon SageMaker Examples
This repository contains example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker
Examples
Introduction to Ground Truth Labeling Jobs
These examples provide quick walkthroughs to get you up and running with the labeling job workflow for Amazon SageMaker Ground Truth.
👉[NeurIPS 2020] This project provides a strong single-stage baseline for Long-Tailed Classification, Detection, and Instance Segmentation (LVIS). It is also a PyTorch implementation of the NeurIPS 2020 paper 'Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect'.
😎TOPICS: ``
⭐️STARS：125, 今日上升数↑:17
👉README：
A Strong Single-Stage Baseline for Long-Tailed Problems
This project provides a strong single-stage baseline for Long-Tailed Classification (under ImageNet-LT, Long-Tailed CIFAR-10/-100 datasets), Detection, and Instance Segmentation (under LVIS dataset). It is also a PyTorch implementation of the NeurIPS 2020 paperLong-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect, which proposes a general solution to remove the bad momentum causal effect for a variety of Long-Tailed Recognition tasks. The codes are organized into three folders:
The classification folder supports long-tailed classification on ImageNet-LT, Long-Tailed CIFAR-10/CIFAR-100 datasets.
The lvis_old folder (deprecated) supports long-tailed object detection and instance segmentation on LVIS V0.5 dataset, which is built on top of mmdet V1.1.
The latest version of long-tailed detection and instance segmentation is under [lvis1.0 ...
👉Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).
😎TOPICS: umap,dimensionality-reduction,semisupervised-learning,representation-learning,machine-learning
⭐️STARS：65, 今日上升数↑:19
👉README：
Parametric UMAP (2020; Code for paper)
This repository contains the code needed to reproduce the results in the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" by Sainburg, McInnes, and Gentner (2020).
Citation:
@Article{parametricumap,
title={Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning},
author={Sainburg, Tim and McInnes, Leland and Gentner, Timothy Q},
}
How to use
The main implementation of this code is available in umap.parametric_umap in the UMAP repository (v0.5+). Most people reading this will want to use that code, and can ignore this repository.
The code in this repository is the 'messy' version. It has custom training loops which are a bit more verbose and customizable. It might be more useful for integrating UMAP into your custom models.
👉A beginner-friendly project to help you in open-source contributions. Made specifically for contributions in HACKTOBERFEST 2020! Algorithms in Python and Machine Learning. Please leave a star ⭐ to support this project! ✨
😎TOPICS: hactoberfest,hactoberfest2020,first-timers,first-pull-request,first-pull-request-and-commit,first-contributions,good-first-issue,open-source,beginner,beginner-friendly,digitalocean,easy-to-use,github,up-for-grabs,machine-learning,python,python3,machinelearning,pr-welcome
⭐️STARS：33, 今日上升数↑:13
👉README：
A beginner friendly project to help you in open source contributions. An attempt to bring all the algorithms together.
The goal of this project is to help the beginners with their contributions in Open Source and bring all the possible algorithms of Machine Learning and Python together. We aim to achieve this collaboratively, so feel free to contribute in any way you want, just make sure to follow the contribution guidelines.
For now, this repo is focused on the beginner frienldy contributions in Hacktoberfest 2020.
The open source community provides a great opportunity for aspiring ...
👉Kubernetes community content
😎TOPICS: kubernetes
⭐️STARS：7034, 今日上升数↑:9
👉README：
Kubernetes Community
Welcome to the Kubernetes community!
This is the starting point for joining and contributing to the Kubernetes community - improving docs, improving code, giving talks etc.
To learn more about the project structure and organization, please refer to [Project Governance] information.
Communicating
The communication page lists communication channels like chat,
issues, mailing lists, conferences, etc.
For more specific topics, try a SIG.
Governance
Kubernetes has the following types of groups that are officially supported:
Committees are named sets of people that are chartered to take on sensitive topics.
This group is encouraged to be as open as possible while achieving its mission but, because of the nature of the topics discussed, private communications are allowed.
Examples of committees include the steering committee and things like security or code of conduct.
Special Interest Groups (SIGs) are persistent open groups that focus on a par...
If you are looking to learn TensorFlow, don't miss the core TensorFlow documentation
which is largely runnable code.
Those notebooks can be opened in Colab from tensorflow.org.
What is this repo?
This is the TensorFlow example repo. It has several classes of material:
👉Repository for the free online book Machine Learning from Scratch (link below!)
😎TOPICS: ``
⭐️STARS：310, 今日上升数↑:34
👉README：
Machine Learning from Scratch
Welcome to the repo for my free online book, "Machine Learning from Scratch".
The book itself can be found here.
(A somewhat ugly version of) the PDF can be found in the book.pdf file above in the master branch. No...
👉:robot: :speech_balloon: Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)
😎TOPICS: deep-learning,mozilla,text-to-speech,python,pytorch,tacotron,tts,speaker-encoder,dataset-analysis,tacotron2,tensorflow2,vocoder,melgan,gantts,multiband-melgan,mozilla-tts,glow-tts,speech
⭐️STARS：2645, 今日上升数↑:6
🤩Python随身听-技术精选： /donnemartin/system-design-primer
👉README：
*English ∙ 日本語 ∙ 简体中文 ∙ 繁體中文 | العَرَبِيَّة ∙ বাংলা ∙ Português do Brasil ∙ Deutsch ∙ ελληνικά ∙ עברית ∙ Italiano ∙ 한국어 ∙ فارسی ∙ Polski ∙ русский язык ∙ Español ∙ [...
地址：https://github.com/donnemartin/system-design-primer
🤩Python随身听-技术精选： /home-assistant/core
👉README：
Home Assistant |Chat Status|
Open source home automation that puts local control and privacy first. Powered by a worldwide community of tinkerers and DIY enthusiasts. Perfect to run on a Raspberry Pi or a local server.
Check out
home-assistant.io <https://home-assistant.io>__ fora demo <https://home-assistant.io/demo/>,installation instructions <https://home-assistant.io/getting-started/>,tutorials <https://home-assistant.io/getting-started/automation-2/>__ anddocumentation <https://home-assistant.io/docs/>__.|screenshot-states|
Featured integrations
|screenshot-components|
The system is built using a modular approach so support for other devices or actions can be implemented easily. See also the `section on architecture <htt...
地址：https://github.com/home-assistant/core
🤩Python随身听-技术精选： /openai/jukebox
👉README：
Status: Archive (code is provided as-is, no updates expected)
Jukebox
Code for "Jukebox: A Generative Model for Music"
Paper
Blog
Explorer
Colab
Install
Install the conda package manager from https://docs.conda.io/en/latest/miniconda.html
Required: Sampling
conda create --name jukebox python=3.7.5
conda activate jukebox
conda install mpi4py=3.0.3 ## if this fails, try: pip install mpi4py==3.0.3
conda install pytorch=1.4 torchvision=0.5 cudatoolkit=10.0 -c pytorch
git clone https://github.com/openai/jukebox.git
cd jukebox
pip install -r requirements.txt
pip install -e .
Required: Training
conda install av=7.0.01 -c conda-forge
pip install ./tensorboardX
Optional: Apex for faster training with fused_adam
conda install pytorch=1.1 torchvision=0.3 cudatoolkit=10.0 -c p...
地址：https://github.com/openai/jukebox
🤩Python随身听-技术精选： /kov4l3nko/MEDUZA
👉README：
MEDUZA
"MEDUZA" ("медуза") means "jellyfish" in Ukrainian 🇺🇦.
What is MEDUZA?
It's a Frida-based tool, my replacement for SSLKillSwitch. I created it for in-house use, but then decided to opensource it. TBH, I hate open source, but the world is full of compromises... :(
How does it work?
It's simple. First time, you run an app without sniffing and use it as usual. MEDUZA is sitting quietly and collecting certificates used by the app to connect servers. Then MEDUZA generates a Frida script that fakes (==upnin) the collected certificates. So you run the app for second time, use the generated script, and catch the traffic with mitmproxy.
Limitations
MEDUZA can only unpin apps using iOS system SSL libs. Some apps (e.g. Instagram) do not use the system SSL libs, they implement some third-party custom SSL stack (for example, Instagram uses OpenSSL statically linked to an Instagram private frameworks, see [InstagramSSLP...
地址：https://github.com/kov4l3nko/MEDUZA
🤩Python随身听-技术精选： /Azure/azure-cli
👉README：
Microsoft Azure CLI
A great cloud needs great tools; we're excited to introduce Azure CLI, our next generation multi-platform command line experience for Azure.
Take a test run now from Azure Cloud Shell!
Installation
Please refer to the install guide for detailed install instructions.
A list of common install issues and their resolutions are available at install troubleshooting.
Developer installation (see below)
Usage
bash
$ az [ group ] [ subgroup ] [ command ] {parameters}
Get Started
Please refer to the "get started" guide for in-depth instructions.
For usage and help content, pass in the
-hparameter, for example:bash
$ az storage -h
$ az vm create -h
Highlights
He...
地址：https://github.com/Azure/azure-cli
🤩Python随身听-技术精选： /mingrammer/diagrams
👉README：
Diagrams
Diagram as Code.
Diagrams lets you draw the cloud system architecture in Python code. It was born for prototyping a new system architecture design without any design tools. You can also describe or visualize the existing system architecture as well. Diagrams currently supports main major providers including:
AWS,Azure,GCP,Kubernetes,Alibaba Cloud,Oracle Cloudetc... It also supportsOn-Premisenodes,SaaSand majorProgrammingframeworks and languages.Diagram as Code also allows you to track the architecture diagram changes in any version control system.
地址：https://github.com/mingrammer/diagrams
🤩Python随身听-技术精选： /django/django
👉README：
======
Django
Django is a high-level Python Web framework that encourages rapid development
and clean, pragmatic design. Thanks for checking it out.
All documentation is in the "
docs" directory and online athttps://docs.djangoproject.com/en/stable/. If you're just getting started,
here's how we recommend you read the docs:
First, read
docs/intro/install.txtfor instructions on installing Django.Next, work through the tutorials in order (
docs/intro/tutorial01.txt,docs/intro/tutorial02.txt, etc.).If you want to set up an actual deployment server, read
docs/howto/deployment/index.txtfor instructions.You'll probably want to read through the topical guides (in
docs/topics)next; from there you can jump to the HOWTOs (in
docs/howto) for specificproblems, and check out the reference (
docs/ref) for gory details.See
docs/READMEfor instructions on building an HTML version of the docs.Docs are updated rigorously. If you find any problems in t...
地址：https://github.com/django/django
🤩Python随身听-技术精选： /public-apis/public-apis
👉README：
A collective list of free APIs for use in software and web development.
A public API for this project can be found here!
For information on contributing to this project, please see the contributing guide.
Please note a passing build status indicates all listed APIs are available since the last update. A failing build status indicates that 1 or more services may be unavailable at the moment.
Index
地址：https://github.com/public-apis/public-apis
🤩Python随身听-技术精选： /parzulpan/real-live
👉README：
RealLive
简体中文 | 繁体中文 | English
桌面端：
使用视频
为什么是它
它解决了什么？
它有什么特性？
它未来会如何？
快速开始
分支说明：
桌面端调试运行：
配置好 Python 开发环境，推荐 Python3.6+。
首次使用时，Fork 后 Clone 该项目，进入 src/real-live-desktop 桌面端项目文件夹，配置 DebugRun.sh后，然后运行
DebugRun.sh。shell
git clone -b dev https://github.com/parzulpan/real-live.git
./DebugRun.sh
非首次使用时，即配置好环境后，...
地址：https://github.com/parzulpan/real-live
🤩Python随身听-技术精选： /anandpawara/Real_Time_Image_Animation
👉README：
Real time Image Animation
The Project is real time application in opencv using first order model
Steps to setup
Step 1: Create virtual environment
Python version : python v3.7.3 or higher
create virual environment : pip install virtualenv
activate virtual environment : virtualenv env
Step 2: Activate virtual environment
For windows : env/Script/activate
For Linux : source env/bin/activate
Step 3 : Install required modules
Install modules : pip install -r requirements.txt
Install pytorch and torchvision : pip install torch===1.0.0 torchvision===0.2.1 -f https://download.pytorch.org/whl/cu100/torch_stable.html
Step 4 : Download cascade file ,weights and model and save in folder named extract
gdown --id 1wCzJP1XJNB04vEORZvPjNz6drkXm5AUK
The file is also availible via direct link on Google's Drive:
https://drive.google.com/uc?id=1wCzJP1XJNB04vEORZvPjNz6drkXm5AUK
On Linux machine : unzip checkpoints.zip...
地址：https://github.com/anandpawara/Real_Time_Image_Animation
🤩Python随身听-技术精选： /apprenticeharper/DeDRM_tools
👉README：
DeDRM_tools
DeDRM tools for ebooks
This is a repository of all the scripts and other tools for removing DRM from ebooks that I could find, committed in date order as best as I could manage. (Except for the Requiem tools for Apple's iBooks, and Convert LIT for Microsoft's .lit ebooks.)
Mostly it tracks the tools released by Apprentice Alf, athough it also includes the individual tools and their histories from before Alf had a blog.
Users should download the latest zip archive.
Developers might be interested in forking the repository, as it contains unzipped versions of those tools that are zipped to make the changes over time easier to follow.
For the latest Amazon KFX format, users of the calibre plugin should also install the KFX Input pl...
地址：https://github.com/apprenticeharper/DeDRM_tools
🤩Python随身听-技术精选： /domokane/FinancePy
👉README：
FinancePy
FinancePy is a python-based library that is currently in beta version. It covers the following functionality:
Although it is written entirely in Python, it can achieve speeds comparable to C++ by using Numba. As a result the user has both the ability to examine the underlying code and the ability to perform pricing and risk at speeds which compare to a library written in C++.
The target audience for this library includes:
地址：https://github.com/domokane/FinancePy
🤩Python随身听-技术精选： /Ciphey/Ciphey
👉README：
Translations
🇮🇩 ID 🇩🇪 DE 🇭🇺 HU
➡️ Documentation | Discord | Installation Guide ⬅️
🤩Python随身听-技术精选： /facebookresearch/SlowFast
👉README：
PySlowFast
PySlowFast is an open source video understanding codebase from FAIR that provides state-of-the-art video classification models with efficient training. This repository includes implementations of the following methods:
Introduction
The goal of PySlowFast is to provide a high-performance, light-weight pytorch codebase provides state-of-the-art video backbones for video understanding research on different tasks (classification, detection, and etc). It is designed in order to support rapid implementation and evaluation of novel video research ideas. PySlowFast includes implementatio...
地址：https://github.com/facebookresearch/SlowFast
🤩Python随身听-技术精选： /ultralytics/yolov5
👉README：
This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. All code and models are under active development, and are subject to modification or deletion without notice. Use at your own risk.
地址：https://github.com/ultralytics/yolov5
🤩Python随身听-技术精选： /Dod-o/Statistical-Learning-Method_Code
👉README：
前言
力求每行代码都有注释，重要部分注明公式来源。具体会追求下方这样的代码，学习者可以照着公式看程序，让代码有据可查。
如果时间充沛的话，可能会试着给每一章写一篇博客。先放个博客链接吧：传送门。
注：其中Mnist数据集已转换为csv格式，由于体积为107M超过限制，改为压缩包形式。下载后务必先将Mnist文件内压缩包直接解压。
另：有意向为这个repo补充第二版无监督部分的大佬下拉到最下方联系我～只要求注释完善即可。我们可以成为好朋友一起冲鸭！！！
实现
第二章 感知机：
博客：统计学习方法|感知机原理剖析及实现
实现：perceptron/perceptron_dichotomy.py
第三章 K近邻：
博客：统计学习方法|K近邻原理剖析及实现
实现：KNN/KNN.py
第四章 朴素贝叶斯：
博客：统计学习方法|朴素贝叶斯原理剖析及实现
实现：NaiveBayes/NaiveBayes.py
第五章 决策树：
博客：[统计学习方法|决策树原理剖析及实现](http://www.pkudodo.com/2018/...
地址：https://github.com/Dod-o/Statistical-Learning-Method_Code
🤩Python随身听-技术精选： /plotly/dash
👉README：
Dash
Dash is a Python framework for building analytical web applications. No JavaScript required.
Built on top of Plotly.js, React and Flask, Dash ties modern UI elements like dropdowns, sliders, and graphs directly to your analytical Python code. Read our tutorial proudly crafted ❤️ by Dash itself.
User Guide
Offline (PDF) Documentation
Dash Docs on Heroku (for corporate network that cannot access plotly.com)
App Samples
To learn more about Dash, read the [extensive announcement letter](https://medium.com/@plotlygraphs/introducing-dash-5ecf...
地址：https://github.com/plotly/dash
🤩Python随身听-技术精选： /getsentry/sentry
👉README：
Users and logs provide clues. Sentry provides answers.
What's Sentry?
Sentry is a service that helps you monitor and fix crashes
in realtime. The server is in Python, but it contains a full API for
sending events from any language, in any application.
Official Sentry SDKs
地址：https://github.com/getsentry/sentry
🤩Python随身听-技术精选： /mirumee/saleor
👉README：
Saleor Commerce
Website | Blog | Twitter | Gitter | Spectrum
🤩Python随身听-技术精选： /scrapy/scrapy
👉README：
======
Scrapy
.. image:: https://img.shields.io/pypi/v/Scrapy.svg
:target: https://pypi.python.org/pypi/Scrapy
:alt: PyPI Version
.. image:: https://img.shields.io/pypi/pyversions/Scrapy.svg
:target: https://pypi.python.org/pypi/Scrapy
:alt: Supported Python Versions
.. image:: https://img.shields.io/travis/scrapy/scrapy/master.svg
:target: https://travis-ci.org/scrapy/scrapy
:alt: Build Status
.. image:: https://img.shields.io/badge/wheel-yes-brightgreen.svg
:target: https://pypi.python.org/pypi/Scrapy
:alt: Wheel Status
.. image:: https://img.shields.io/codecov/c/github/scrapy/scrapy/master.svg
:target: https://codecov.io/github/scrapy/scrapy?branch=master
:alt: Coverage report
.. image:: https://anaconda.org/conda-forge/scrapy/badges/version.svg
:target: https://anaconda.org/conda-forge/scrapy
:alt: Conda Version
Overview
Scrapy is a fast high-level web crawling and web scraping framework, used to
crawl websites and extract structured data from t...
地址：https://github.com/scrapy/scrapy
🤩Python随身听-技术精选： /HuiZeng/Image-Adaptive-3DLUT
👉README：
Image-Adaptive-3DLUT
Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time
Downloads
Paper, Supplementary, Dataset, [PCT patent]
The whole datasets used in the paper are over 300G. Here I only provided the FiveK dataset resized into 480p resolution (including 8-bit sRGB, 16-bit XYZ inputs and 8-bit sRGB targets). I also provided 10 full-resolution images for testing speed. To obtain the entire full-resolution images, it is recommended to transform from the original FiveK dataset.
A model trained on the 480p resolution can be directly applied to images of 4K (or higher) resolution without performance drop. This can significantly speedup the trainin...
地址：https://github.com/HuiZeng/Image-Adaptive-3DLUT
🤩Python随身听-技术精选： /matterport/Mask_RCNN
👉README：
Mask R-CNN for Object Detection and Segmentation
This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.
The repository includes:
The code is documented and designed to be easy to extend. If you use it in your research, please consider citing this repository (bibtex below). If you work on 3D vision, you might find our recently released Matterport3D dataset useful as well.
This dataset was ...
地址：https://github.com/matterport/Mask_RCNN
🤩Python随身听-技术精选： /lmoroney/dlaicourse
👉README：
...
地址：https://github.com/lmoroney/dlaicourse
🤩Python随身听-技术精选： /Kulbear/deep-learning-coursera
👉README：
Deep Learning Specialization on Coursera
Master Deep Learning, and Break into AI
Instructor: Andrew Ng
Introduction
This repo contains all my work for this specialization. All the code base, quiz questions, screenshot, and images, are taken from, unless specified, Deep Learning Specialization on Coursera.
What I want to say
VERBOSE CONTENT WARNING: YOU CAN JUMP TO THE NEXT SECTION IF YOU WANT
As a CS major student and a long-time self-taught learner, I have completed many CS related MOOCs on Coursera, Udacity, Udemy, and Edx. I do understand the hard time you spend on understanding new concepts and debugging your program. There are discussion forums on most MOOC platforms, however, even a question with detailed description may need some time to be answered. Here I released these solutions, which are only for your reference purpose. It may help you to save some time. And I hope you don't copy any pa...
地址：https://github.com/Kulbear/deep-learning-coursera
🤩Python随身听-技术精选： /ageron/handson-ml2
👉README：
Machine Learning Notebooks
This project aims at teaching you the fundamentals of Machine Learning in
python. It contains the example code and solutions to the exercises in the second edition of my O'Reilly book Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow:
Note: If you are looking for the first edition notebooks, check out ageron/handson-ml.
Quick Start
Want to play with these notebooks online without having to install anything?
Use any of the following services.
WARNING: Please be aware that these services provide temporary environments: anything you do will be deleted after a while, so make sure you download any data you care about.
地址：https://github.com/ageron/handson-ml2
🤩Python随身听-技术精选： /awslabs/amazon-sagemaker-examples
👉README：
Amazon SageMaker Examples
This repository contains example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker
Examples
Introduction to Ground Truth Labeling Jobs
These examples provide quick walkthroughs to get you up and running with the labeling job workflow for Amazon SageMaker Ground Truth.
地址：https://github.com/awslabs/amazon-sagemaker-examples
🤩Python随身听-技术精选： /Pierian-Data/Complete-Python-3-Bootcamp
👉README：
Complete-Python-3-Bootcamp
Course Files for Complete Python 3 Bootcamp Course on Udemy
Get it now for ...
地址：https://github.com/Pierian-Data/Complete-Python-3-Bootcamp
🤩Python随身听-技术精选： /Atcold/pytorch-Deep-Learning
👉README：
This notebook repository now has a companion website, where all the course material can be found in video and textual format.
🇬🇧 🇨🇳 🇰🇷 🇪🇸 🇮🇹 🇹🇷 🇯🇵 [🇸🇦](https://github.com/Atcold/pytorch-Deep-Learning/blob/master/docs/ar/README-AR.m...
地址：https://github.com/Atcold/pytorch-Deep-Learning
🤩Python随身听-技术精选： /KaihuaTang/Long-Tailed-Recognition.pytorch
👉README：
A Strong Single-Stage Baseline for Long-Tailed Problems
This project provides a strong single-stage baseline for Long-Tailed Classification (under ImageNet-LT, Long-Tailed CIFAR-10/-100 datasets), Detection, and Instance Segmentation (under LVIS dataset). It is also a PyTorch implementation of the NeurIPS 2020 paper Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect, which proposes a general solution to remove the bad momentum causal effect for a variety of Long-Tailed Recognition tasks. The codes are organized into three folders:
地址：https://github.com/KaihuaTang/Long-Tailed-Recognition.pytorch
🤩Python随身听-技术精选： /timsainb/ParametricUMAP_paper
👉README：
Parametric UMAP (2020; Code for paper)
This repository contains the code needed to reproduce the results in the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" by Sainburg, McInnes, and Gentner (2020).
Citation:
@Article{parametricumap,
title={Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning},
author={Sainburg, Tim and McInnes, Leland and Gentner, Timothy Q},
}
How to use
The main implementation of this code is available in
umap.parametric_umapin the UMAP repository (v0.5+). Most people reading this will want to use that code, and can ignore this repository.The code in this repository is the 'messy' version. It has custom training loops which are a bit more verbose and customizable. It might be more useful for integrating UMAP into your custom models.
The code can be installed w...
地址：https://github.com/timsainb/ParametricUMAP_paper
🤩Python随身听-技术精选： /geekquad/AlgoBook
👉README：
A beginner friendly project to help you in open source contributions. An attempt to bring all the algorithms together.Please see the Contributing Guidelines .
Join the community on Slack.
Overview
The goal of this project is to help the beginners with their contributions in Open Source and bring all the possible algorithms of Machine Learning and Python together. We aim to achieve this collaboratively, so feel free to contribute in any way you want, just make sure to follow the contribution guidelines.
For now, this repo is focused on the beginner frienldy contributions in Hacktoberfest 2020.
The open source community provides a great opportunity for aspiring ...
地址：https://github.com/geekquad/AlgoBook
🤩Python随身听-技术精选： /kubernetes/community
👉README：
Kubernetes Community
Welcome to the Kubernetes community!
This is the starting point for joining and contributing to the Kubernetes community - improving docs, improving code, giving talks etc.
To learn more about the project structure and organization, please refer to [Project Governance] information.
Communicating
The communication page lists communication channels like chat,
issues, mailing lists, conferences, etc.
For more specific topics, try a SIG.
Governance
Kubernetes has the following types of groups that are officially supported:
This group is encouraged to be as open as possible while achieving its mission but, because of the nature of the topics discussed, private communications are allowed.
Examples of committees include the steering committee and things like security or code of conduct.
地址：https://github.com/kubernetes/community
🤩Python随身听-技术精选： /tensorflow/docs
👉README：
TensorFlow Documentation
These are the source files for the guide and tutorials on
tensorflow.org.
To contribute to the TensorFlow documentation, please read
CONTRIBUTING.md, the
TensorFlow docs contributor guide,
and the style guide.
To file a docs issue, use the issue tracker in the
[tensorflow/tensorflow](https://github.com/tensorflow/tensorflow/issues/new?template=20-docum...
地址：https://github.com/tensorflow/docs
🤩Python随身听-技术精选： /CoreyMSchafer/code_snippets
👉README：
code_...
地址：https://github.com/CoreyMSchafer/code_snippets
🤩Python随身听-技术精选： /tensorflow/examples
👉README：
TensorFlow Examples
Most important links!
If you are looking to learn TensorFlow, don't miss the
core TensorFlow documentation
which is largely runnable code.
Those notebooks can be opened in Colab from
tensorflow.org.
What is this repo?
This is the TensorFlow example repo. It has several classes of material:
地址：https://github.com/tensorflow/examples
🤩Python随身听-技术精选： /Mikoto10032/DeepLearning
👉README：
DeepLearning Tutorial
一. 入门资料
完备的 AI 学习路线，最详细的中英文资源整理 ⭐
AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NL
Machine-Learning
数学基础
机器学习基础
快速入门
地址：https://github.com/Mikoto10032/DeepLearning
🤩Python随身听-技术精选： /dafriedman97/mlbook
👉README：
Machine Learning from Scratch
Welcome to the repo for my free online book, "Machine Learning from Scratch".
The book itself can be found here.
(A somewhat ugly version of) the PDF can be found in the book.pdf file above in the
masterbranch. No...地址：https://github.com/dafriedman97/mlbook
🤩Python随身听-技术精选： /mozilla/TTS
👉README：
" width="320" height="95" />
This project is a part of Mozilla Common Voice.
Mozilla TTS aims a deep learning based Text2Speech engine, low in cost and high in quality.
You can check some of synthesized voice samples from here.
If you are new, you can also find here a brief post about some of TTS architectures and here list of up-to-date research papers.
TTS Performance
🤩Python随身听-技术精选： /rasbt/deeplearning-models
👉README：
Deep Learning Models
A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
Traditional Machine Learning
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | [Nbviewer](https://nbviewer.jupyter.org/github/rasbt/deeplearning-...
地址：https://github.com/rasbt/deeplearning-models
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