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👉An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
The tool manages automated machine learning (AutoML) experiments, dispatches and runs experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different training environments like Local Machine, Remote Servers, OpenPAI, Kubeflow, <a href=...
When I first learned Python nearly 25 years ago, I was immediately
struck by how I could productively apply it to all sorts of messy work
projects. Fast-forward a decade and I found myself teaching others the
same fun. The result of that teaching is this course--A no-nonsense
treatment of Python that has been actively taught to more than 400
in-person groups since 2007. Traders, systems admins, astronomers,
tinkerers, and even a few hundred rocket scientists who used Python to
help land a rover on Mars--they've all taken this course. Now, I'm
pleased to make it available under a Creative Commons license. Enjoy!
The material you see here is the heart of an instructor-led Python
training course used for corporate training and professional
development. It has been in continual development since 2007 and
battle tested in real-world classrooms. U...
👉A Python program to scrape secrets from GitHub through usage of a large repository of dorks.
😎TOPICS: ``
⭐️STARS：348, 今日上升数↑:150
👉README：
GitDorker
GitDorker is a tool that utilizes the GitHub Search API and an extensive list of GitHub dorks that I've compiled from various sources to provide an overview of sensitive information stored on github given a search query.
The Primary purpose of GitDorker is to provide the user with a clean and tailored attack surface to begin harvesting sensitive information on GitHub. GitDorker can be used with additional tools such as GitRob or Trufflehog on interesting repos or users discovered from GitDorker to produce best results.
Rate Limits
GitDorker utilizes the GitHub Search API and is limited to 30 requests per minute. In order to prevent rate limites a sleep function is built into GitDorker after every 30 requests to prevent search failures. Therefore, if one were to run use the alldorks.txt file with GitDorker, the process will take roughly 5 minutes to complete.
Requirements
** Python3
** GitHub Personal Access Token
** Install requirements inside of the requirements.txt file of this ...
GHunt is an OSINT tool to extract information from any Google Account using an email.
It can currently extract:
Owner's name
Last time the profile was edited
Google ID
If the account is a Hangouts Bot
Activated Google services (YouTube, Photos, Maps, News360, Hangouts, etc.)
Possible YouTube channel
Possible other usernames
Public photos (P)
Phones models (P)
Phones firmwares (P)
Installed softwares (P)
Google Maps reviews (M)
Possible physical location (M)
The features marked with a (P) require the target account to have the default setting of Allow the people you share content with to download your photos and videos on the Google AlbumArchive, or if the target has ever used Picasa linked to their Google account.
More info here.
Those marked with a (M) require the Google Maps reviews of the target to be public (they are by default).
Manim is an animation engine for explanatory math videos. It's used to create precise animations programmatically, as seen in the videos at 3Blue1Brown.
This repository contains the version of manim used by 3Blue1Brown. There is also a community maintained version at https://github.com/ManimCommunity/manim/.
To get help or to join the development effort, please join the discord.
Installation
Manim runs on Python 3.6 or higher version. You can install it from PyPI via pip:
pip3 install manimlib
System requirements are cairo, ffmpeg, sox (optional, if you want to play the prompt tone after running), latex (optional, if you want to use LaTeX).
You can now use it via the manim command. For example:
manim my_project.py MyScene
For more options, take a look at the Using manim sections further below.
Most of the examples are full-fledged VM examples, which use Vagrant, VirtualBox, and Ansible to boot and configure VMs on your local workstation. Not all playbooks follow all of Ansible's best practices, as they illustrate particular Ansible features in an instructive manner.
SV2TTS is a three-stage deep learning framework that allows to create a numerical representation of a voice from a few seconds of audio, and to use it to condition a text-to-speech model trained to generalize to new voices.
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/>__.
All datasets in this repository are released under the CC BY 4.0 International
license, which can be found here: https://creativecommons.org/licenses/by/4.0/legalcode. All source files in this
repository are released under the Apache 2.0 license, the text of which can be
found in the LICENSE file.
Python随身听-2020-10-17-技术精选
🤩Python随身听-技术精选： /microsoft/nni
👉An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
😎TOPICS:
automl,deep-learning,neural-architecture-search,hyperparameter-optimization,distributed,bayesian-optimization,automated-machine-learning,machine-learning,machine-learning-algorithms,data-science,tensorflow,pytorch,neural-network,deep-neural-network,model-compression,feature-engineering,automated-feature-engineering,nas,python,feature-extraction⭐️STARS：7285, 今日上升数↑:188
👉README：
简体中文
NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate Feature Engineering, Neural Architecture Search, Hyperparameter Tuning and Model Compression.
The tool manages automated machine learning (AutoML) experiments, dispatches and runs experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different training environments like Local Machine, Remote Servers, OpenPAI, Kubeflow, <a href=...
地址：https://github.com/microsoft/nni
🤩Python随身听-技术精选： /deepinsight/insightface
👉Face Analysis Project on MXNet
😎TOPICS:
face-recognition,face-detection,mxnet,face-alignment,age-estimation,arcface,retinaface⭐️STARS：7739, 今日上升数↑:63
👉README：
InsightFace: 2D and 3D Face Analysis Project
By Jia Guo and Jiankang Deng
License
The code of InsightFace is released under the MIT License. There is no limitation for both acadmic and commercial usage.
The training data containing the annotation (and the models trained with these data) are available for non-commercial research purposes only.
Introduction
InsightFace is an open source 2D&3D deep face analysis toolbox, mainly based on MXNet.
The master branch works with MXNet 1.2 to 1.6, with Python 3.x.
ArcFace Video Demo
Please click the image to watch the Youtube video. For Bilibili users, click here.
Recent Update
2020-10-13: A new training method and one large training set(360K IDs) were released here by DeepGlint.2020-10-09: We opened a large scale recog...地址：https://github.com/deepinsight/insightface
🤩Python随身听-技术精选： /huggingface/transformers
👉🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
😎TOPICS:
nlp,natural-language-processing,natural-language-understanding,pytorch,language-model,natural-language-generation,tensorflow,bert,gpt,xlnet,language-models,xlm,transformer-xl,pytorch-transformers⭐️STARS：35189, 今日上升数↑:121
👉README：
🤩Python随身听-技术精选： /dabeaz-course/practical-python
👉Practical Python Programming (course by @dabeaz)
😎TOPICS: ``
⭐️STARS：5580, 今日上升数↑:54
👉README：
Welcome!
When I first learned Python nearly 25 years ago, I was immediately
struck by how I could productively apply it to all sorts of messy work
projects. Fast-forward a decade and I found myself teaching others the
same fun. The result of that teaching is this course--A no-nonsense
treatment of Python that has been actively taught to more than 400
in-person groups since 2007. Traders, systems admins, astronomers,
tinkerers, and even a few hundred rocket scientists who used Python to
help land a rover on Mars--they've all taken this course. Now, I'm
pleased to make it available under a Creative Commons license. Enjoy!
GitHub Pages | GitHub Repo.
What is This?
The material you see here is the heart of an instructor-led Python
training course used for corporate training and professional
development. It has been in continual development since 2007 and
battle tested in real-world classrooms. U...
地址：https://github.com/dabeaz-course/practical-python
🤩Python随身听-技术精选： /obheda12/GitDorker
👉A Python program to scrape secrets from GitHub through usage of a large repository of dorks.
😎TOPICS: ``
⭐️STARS：348, 今日上升数↑:150
👉README：
GitDorker
GitDorker is a tool that utilizes the GitHub Search API and an extensive list of GitHub dorks that I've compiled from various sources to provide an overview of sensitive information stored on github given a search query.
The Primary purpose of GitDorker is to provide the user with a clean and tailored attack surface to begin harvesting sensitive information on GitHub. GitDorker can be used with additional tools such as GitRob or Trufflehog on interesting repos or users discovered from GitDorker to produce best results.
Rate Limits
GitDorker utilizes the GitHub Search API and is limited to 30 requests per minute. In order to prevent rate limites a sleep function is built into GitDorker after every 30 requests to prevent search failures. Therefore, if one were to run use the alldorks.txt file with GitDorker, the process will take roughly 5 minutes to complete.
Requirements
** Python3
** GitHub Personal Access Token
** Install requirements inside of the requirements.txt file of this ...
地址：https://github.com/obheda12/GitDorker
🤩Python随身听-技术精选： /mxrch/GHunt
👉🕵️♂️ Investigate Google Accounts with emails.
😎TOPICS: ``
⭐️STARS：4473, 今日上升数↑:264
👉README：
Description
GHunt is an OSINT tool to extract information from any Google Account using an email.
It can currently extract:
The features marked with a (P) require the target account to have the default setting of
Allow the people you share content with to download your photos and videoson the Google AlbumArchive, or if the target has ever used Picasa linked to their Google account.More info here.
Those marked with a (M) require the Google Maps reviews of the target to be public (they are by default).
Screenshots
🤩Python随身听-技术精选： /3b1b/manim
👉Animation engine for explanatory math videos
😎TOPICS:
python,animation,explanatory-math-videos,3b1b-videos⭐️STARS：26052, 今日上升数↑:55
👉README：
Manim is an animation engine for explanatory math videos. It's used to create precise animations programmatically, as seen in the videos at 3Blue1Brown.
This repository contains the version of manim used by 3Blue1Brown. There is also a community maintained version at https://github.com/ManimCommunity/manim/.
To get help or to join the development effort, please join the discord.
Installation
Manim runs on Python 3.6 or higher version. You can install it from PyPI via pip:
pip3 install manimlib
System requirements are cairo, ffmpeg, sox (optional, if you want to play the prompt tone after running), latex (optional, if you want to use LaTeX).
You can now use it via the
manimcommand. For example:manim my_project.py MyScene
For more options, take a look at the Using manim sections further below.
###...
地址：https://github.com/3b1b/manim
🤩Python随身听-技术精选： /geerlingguy/ansible-for-devops
👉Ansible for DevOps examples.
😎TOPICS:
ansible,devops,vagrant,examples,jeff-geerling,book,leanpub,amazon,kindle,docker,playbook,kubernetes,aws⭐️STARS：3670, 今日上升数↑:132
👉README：
Ansible for DevOps Examples
This repository contains Ansible examples developed to support different sections of Ansible for DevOps, a book on Ansible by Jeff Geerling.
Most of the examples are full-fledged VM examples, which use Vagrant, VirtualBox, and Ansible to boot and configure VMs on your local workstation. Not all playbooks follow all of Ansible's best practices, as they illustrate particular Ansible features in an instructive manner.
For more interesting examples of what you can do with Ansible, please see the Ansible Vagrant Examples repository, and browse through some of geerlingguy's roles on Ansible Galaxy.
Examples and Chapters in which they're used
Here is an outline of all the examples contained in this repository, by chapter:
Chapter 1
Chapter 2
地址：https://github.com/geerlingguy/ansible-for-devops
🤩Python随身听-技术精选： /CorentinJ/Real-Time-Voice-Cloning
👉Clone a voice in 5 seconds to generate arbitrary speech in real-time
😎TOPICS:
deep-learning,pytorch,tensorflow,tts,voice-cloning,python⭐️STARS：20958, 今日上升数↑:91
👉README：
Real-Time Voice Cloning
This repository is an implementation of Transfer Learning from Speaker Verification to
Multispeaker Text-To-Speech Synthesis (SV2TTS) with a vocoder that works in real-time. Feel free to check my thesis if you're curious or if you're looking for info I haven't documented. Mostly I would recommend giving a quick look to the figures beyond the introduction.
SV2TTS is a three-stage deep learning framework that allows to create a numerical representation of a voice from a few seconds of audio, and to use it to condition a text-to-speech model trained to generalize to new voices.
Video demonstration (click the picture):
Papers implemented
地址：https://github.com/CorentinJ/Real-Time-Voice-Cloning
🤩Python随身听-技术精选： /s0md3v/XSStrike
👉Most advanced XSS scanner.
😎TOPICS:
xss,xss-scanner,xss-exploit,xss-bruteforce,xss-python,xss-detection,xsstrike,waf-detection⭐️STARS：8459, 今日上升数↑:13
👉README：
XSStrike
Advanced XSS Detection Suite
XSStrike Wiki • Usage • FAQ • For Developers • 地址：https://github.com/s0md3v/XSStrike
🤩Python随身听-技术精选： /home-assistant/core
👉: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,hacktoberfest⭐️STARS：36293, 今日上升数↑:29
👉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 <https://developers.home-assistant.io/docs/en/architectu...
地址：https://github.com/home-assistant/core
🤩Python随身听-技术精选： /apache/airflow
👉Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
😎TOPICS:
airflow,apache,apache-airflow,python,scheduler,workflow,hacktoberfest⭐️STARS：18567, 今日上升数↑:17
👉README：
Install
Shap can be installed from either ...
地址：https://github.com/slundberg/shap
🤩Python随身听-技术精选： /google-research/google-research
👉Google Research
😎TOPICS:
machine-learning,ai,research⭐️STARS：13029, 今日上升数↑:14
👉README：
Google Research
This repository contains code released by
Google Research.
All datasets in this repository are released under the CC BY 4.0 International
license, which can be found here:
https://creativecommons.org/licenses/by/4.0/legalcode. All source files in this
repository are released under the Apache 2.0 license, the text of which can be
found in the LICENSE file.
Because the re...
地址：https://github.com/google-research/google-research
🤩Python随身听-技术精选： /CoreyMSchafer/code_snippets
👉None
😎TOPICS: ``
⭐️STARS：5882, 今日上升数↑:11
👉README：
code_...
地址：https://github.com/CoreyMSchafer/code_snippets
🤩Python随身听-技术精选： /mertensu/transformer-tutorial
👉Visualising the Transformer encoder
😎TOPICS: ``
⭐️STARS：82, 今日上升数↑:14
👉README：
Transformer encoder - visualized
This repo contains the slides and the notebook
You can take a look at the notebook using NBViewer:
[nbviewer-link](https://nbviewer.jupyter.org/github/mertensu/transformer-tutorial/blob/master/tr...
地址：https://github.com/mertensu/transformer-tutorial
🤩Python随身听-技术精选： /zergtant/pytorch-handbook
👉pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行
😎TOPICS:
pytorch,pytorch-tutorials,pytorch-handbook,deep-learning,neural-network,machine-learning⭐️STARS：12714, 今日上升数↑:14
👉README：
PyTorch 中文手册（pytorch handbook）
书籍介绍
这是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门。
由于本人水平有限，在写此教程的时候参考了一些网上的资料，在这里对他们表示敬意，我会在每个引用中附上原文地址，方便大家参考。
深度学习的技术在飞速的发展，同时PyTorch也在不断更新，且本人会逐步完善相关内容。
版本说明
由于PyTorch版本更迭，教程的版本会与PyTorch版本，保持一致。
当前版本 1.6
QQ 4群
群号：884017356
![PyTorch Handbook 交流4群]( "PyTorch Handbook 交流4群")
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点击链接加入群聊 『PyTorch Handbook 交流4群』
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说明
修改错别字请直接提issue或PR
PR时请注意版本
有问题也请直接提issue
感谢
目录
第一章：PyTorch 入门
地址：https://github.com/zergtant/pytorch-handbook
🤩Python随身听-技术精选： /fastai/fastai
👉The fastai deep learning library, plus lessons and tutorials
😎TOPICS: ``
⭐️STARS：19676, 今日上升数↑:12
👉README：
...
地址：https://github.com/fastai/fastai
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