You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
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...
👉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=...
Implementation of λ Networks, a new approach to image recognition that reaches SOTA on ImageNet. The new method utilizes λ layer, which captures interactions by transforming contexts into linear functions, termed lambdas, and applying these linear functions to each input separately.
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/>__.
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.
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
We present a generic image-to-image translation framework, Pixel2Style2Pixel (pSp). Our pSp framework is based on a novel encoder network that directly generates a series of style vectors which are fed into a pretrained StyleGAN generator, forming the extended W+ latent space. We first show that our encoder can directly embed real images into W+, with no additional optimization. We further introduce a dedicated identity loss which is shown to achieve improved performance in the reconstruction of an input image. We demonstrate pSp to be a simple architecture that, by leveraging a well-trained, fixed generator network, can be easily applied on a wide-range of image-to-image translation tasks. Solving these t...
👉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.
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.
The videos on the left show the driving videos. The first row on the right for each dataset shows the source videos. The bottom row contains the animated sequences with motion transferred from the driving video and object taken from the source image. We trained a separate network for each task.
VoxCeleb Dataset
Fashion Dataset
MGIF Dataset
Installation
We support python3. To install the dependencies run:
pip install -r requirements.txt
YAML configs
There are several configuration (config/dataset_name.yaml) files one for each `da...
This repository contains the exercises and its solution contained in the book An Introduction to Statistical Learning
An-Introduction-to-Statistical-Learning is one of the most popular books among data scientists to learn the conepts and intuitions behind
machine learning algorithms, however, the exercises are implemented in R language, which is a hinderence for all those who are using python
language. To overcome this i have tried solving all the questions in practical exerices in Python language, so people using python language
can also get the most our of this amazing book. Along with that i have also provided the solutions for conceptual questions.
I had tried my best to write the correct solutions to the problem, It was a challenge, and i need to learn to do a lot of research. I do not gurantee that all the solutions are
absoletely correct. I have commented the notebooks.
If you find any query, do send a fe...
These notebooks cover an introduction to deep learning, fastai, and PyTorch. fastai is a layered API for deep learning; for more information, see the fastai paper. Everything in this repo is copyright Jeremy Howard and Sylvain Gugger, 2020 onwards.
These notebooks are used for a MOOC and form the basis of this book, which is currently available for purchase. It does not have the same GPL restrictions that are on this draft.
The code in the notebooks and python .py files is covered by the GPL v3 license; see the LICENSE file for details.
The remainder (including all markdown cells in the notebooks and other prose) is not licensed for any redistribution or change ...
Python随身听-2020-10-21-技术精选
🤩Python随身听-技术精选： /TheAlgorithms/Python
👉All Algorithms implemented in Python
😎TOPICS:
python,algorithm,algorithms-implemented,algorithm-competitions,algos,sorts,searches,sorting-algorithms,education,learn,practice,community-driven,interview,hacktoberfest⭐️STARS：89999, 今日上升数↑:220
👉README：
The Algorithms - Python
All algorithms implemented in Python (for education)
These implementations are for learning purposes only. Therefore they may be less efficient than the implementations in the Python standard library.
Contri...
地址：https://github.com/TheAlgorithms/Python
🤩Python随身听-技术精选： /dabeaz-course/practical-python
👉Practical Python Programming (course by @dabeaz)
😎TOPICS: ``
⭐️STARS：6136, 今日上升数↑:190
👉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： /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：7974, 今日上升数↑:204
👉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： /adamerose/pandasgui
👉A GUI for Pandas DataFrames
😎TOPICS:
pandas,dataframe,gui,viewer⭐️STARS：630, 今日上升数↑:72
👉README：
PandasGUI
A GUI for analyzing Pandas DataFrames.
Demo
Installation
Install latest release from PyPi:
pip install pandasgui
Install directly from Github for the latest unreleased changes:
pip install git+https://github.com/adamerose/pandasgui.git
Usage
Create and view a simple DataFrame
import pandas as pd
from pandasgui import show
df = pd.DataFrame(([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=['a', 'b', 'c'])
show(df)
If you are running your code as a script instead of in IPython or Jupyter, you need to do this instead:
This will pause the script until you close the GUI
show(df, settings={'block': True})
PandasGUI comes with sample datasets that will download on first use. You can also import `all_datasets...
地址：https://github.com/adamerose/pandasgui
🤩Python随身听-技术精选： /lucidrains/lambda-networks
👉Implementation of LambdaNetworks, a new approach to image recognition that reaches SOTA with less compute
😎TOPICS:
artificial-intelligence,deep-learning,computer-vision,attention-mechanism,attention⭐️STARS：588, 今日上升数↑:169
👉README：
Lambda Networks - Pytorch
Implementation of λ Networks, a new approach to image recognition that reaches SOTA on ImageNet. The new method utilizes λ layer, which captures interactions by transforming contexts into linear functions, termed lambdas, and applying these linear functions to each input separately.
Yannic Kilcher's paper review
Install
$ pip install lambda-networks
Usage
Global context
import torch
from lambda_networks import LambdaLayer
layer = LambdaLayer(
dim = 32, # channels going in
dim_out = 32, # channels out
n = 64 * 64, # number of input pixels (64 x 64 image)
dim_k = 16, # key dimension
heads = 4, # number of heads, for multi-query
dim_u = 1 # 'intra-depth' dimension
)
x = torch.randn(1, 32, 64, 64)
layer(x) # (1, 32, 64, 64)
Localized context
import torch
from lambda_networks import LambdaLayer
layer = LambdaLayer(
...
地址：https://github.com/lucidrains/lambda-networks
🤩Python随身听-技术精选： /donnemartin/system-design-primer
👉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：109736, 今日上升数↑:99
👉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： /deepfakes/faceswap
👉Deepfakes Software For All
😎TOPICS:
faceswap,face-swap,deep-learning,deeplearning,deep-neural-networks,deepfakes,deepface,deep-face-swap,fakeapp,neural-networks,neural-nets,openfaceswap,myfakeapp,machine-learning⭐️STARS：32830, 今日上升数↑:25
👉README：
deepfakes_faceswap
FaceSwap is a tool that utilizes deep learning to recognize and swap faces in pictures and videos.
Jennifer Lawrence/Steve Buscemi FaceSwap using the Villain model
Make sure you check out INSTALL.md before getting started.
...
地址：https://github.com/deepfakes/faceswap
🤩Python随身听-技术精选： /tiangolo/full-stack-fastapi-postgresql
👉Full stack, modern web application generator. Using FastAPI, PostgreSQL as database, Docker, automatic HTTPS and more.
😎TOPICS:
python,python3,json,json-schema,docker,postgresql,frontend,backend,fastapi,traefik,letsencrypt,swagger,celery,jwt,vue,vuex,cookiecutter,openapi,openapi3,pgadmin⭐️STARS：3423, 今日上升数↑:13
👉README：
Full Stack FastAPI and PostgreSQL - Base Project Generator
Generate a backend and frontend stack using Python, including interactive API documentation.
Interactive API documentation
Alternative API documentation
Dashboard Login
Dashboard - Create User
Features
地址：https://github.com/tiangolo/full-stack-fastapi-postgresql
🤩Python随身听-技术精选： /numpy/numpy
👉The fundamental package for scientific computing with Python.
😎TOPICS:
numpy,python⭐️STARS：15191, 今日上升数↑:12
👉README：
NumPy is the fundamental package needed for scientific computing with Python.
It provides:
地址：https://github.com/numpy/numpy
🤩Python随身听-技术精选： /sherlock-project/sherlock
👉🔎 Hunt down social media accounts by username across social networks
😎TOPICS:
osint,reconnaissance,linux,macos,cli,sherlock,python3,windows,redteam,tools,information-gathering⭐️STARS：15158, 今日上升数↑:12
👉README：
Hunt down social media accounts by username across social networks
🤩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：36377, 今日上升数↑:27
👉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： /keras-team/keras
👉Deep Learning for humans
😎TOPICS:
deep-learning,tensorflow,neural-networks,machine-learning,data-science,python⭐️STARS：50047, 今日上升数↑:12
👉README：
# Keras: Deep Learning for humans
Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow.
Read the documentation at Keras.io.
Multi-backend Keras and tf.keras
**Multi-backend Keras has been discontinued. At this time, we recommend that Keras us...
地址：https://github.com/keras-team/keras
🤩Python随身听-技术精选： /public-apis/public-apis
👉A collective list of free APIs for use in software and web development.
😎TOPICS: ``
⭐️STARS：98364, 今日上升数↑:67
👉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： /python-telegram-bot/python-telegram-bot
👉We have made you a wrapper you can't refuse
😎TOPICS:
python,telegram,bot,chatbot,framework,hacktoberfest⭐️STARS：12039, 今日上升数↑:16
👉README：
.. image:: https://github.com/python-telegram-bot/logos/blob/master/logo-text/png/ptb-logo-text_768.png?raw=true
:align: center
:target: https://python-telegram-bot.org
:alt: python-telegram-bot Logo
We have made you a wrapper you can't refuse
We have a vibrant community of developers helping each other in our
Telegram group <https://telegram.me/pythontelegrambotgroup>_. Join us!Stay tuned for library updates and new releases on our
Telegram Channel <https://telegram.me/pythontelegrambotchannel>_... image:: https://img.shields.io/pypi/v/python-telegram-bot.svg
:target: https://pypi.org/project/python-telegram-bot/
:alt: PyPi Package Version
.. image:: https://img.shields.io/pypi/pyversions/python-telegram-bot.svg
:target: https://pypi.org/project/python-telegram-bot/
:alt: Supported Python versions
.. image:: https://cpu.re/static/python-telegram-bot/downloads.svg
:target: https://www.cpu.re/static/python-telegram-bot/downloads-by-python-version.txt
:alt: PyPi Package ...
地址：https://github.com/python-telegram-bot/python-telegram-bot
🤩Python随身听-技术精选： /juntang-zhuang/Adabelief-Optimizer
👉Repository for NeurIPS 2020 Spotlight "AdaBelief Optimizer: Adapting stepsizes by the belief in observed gradients"
😎TOPICS: ``
⭐️STARS：459, 今日上升数↑:64
👉README：
AdaBelief Optimizer
NeurIPS 2020 Spotlight, trains fast as Adam, generalizes well as SGD, and is stable to train GANs.
Table of Contents
External Links
Project Page, arXiv , Reddit , Twitter
Quick Guide
AdaBelief uses a different denominat...
地址：https://github.com/juntang-zhuang/Adabelief-Optimizer
🤩Python随身听-技术精选： /codebasics/py
👉Repository to store sample python programs for python learning
😎TOPICS:
python,pandas,pandas-dataframe,pandas-tutorial,numpy,numpy-arrays,numpy-tutorial,python-tutorial,python-tutorials,python-pandas,jupyter-notebook,jupyter,jupyter-notebooks,jupyter-tutorial⭐️STARS：1243, 今日上升数↑:16
👉README：
py
Repository to store sample python programs for python learning
Yo...
地址：https://github.com/codebasics/py
🤩Python随身听-技术精选： /eladrich/pixel2style2pixel
👉Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation"
😎TOPICS:
image-translation,stylegan,generative-adversarial-network,stylegan-encoder⭐️STARS：586, 今日上升数↑:32
👉README：
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
地址：https://github.com/eladrich/pixel2style2pixel
🤩Python随身听-技术精选： /ageron/handson-ml2
👉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：10820, 今日上升数↑:21
👉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： /Pierian-Data/Complete-Python-3-Bootcamp
👉Course Files for Complete Python 3 Bootcamp Course on Udemy
😎TOPICS: ``
⭐️STARS：12531, 今日上升数↑:12
👉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： /fengdu78/lihang-code
👉《统计学习方法》的代码实现
😎TOPICS: ``
⭐️STARS：12692, 今日上升数↑:17
👉README：
《统计学习方法》第二版的代码实现
李航老师编写的《统计学习方法》全面系统地介绍了统计学习的主要方法，特别是监督学习方法，包括感知机、k近邻法、朴素贝叶斯法、决策树、逻辑斯谛回归与支持向量机、提升方法、em算法、隐马尔可夫模型和条件随机场等。除第1章概论和最后一章总结外，每章介绍一种方法。叙述从具体问题或实例入手，由浅入深，阐明思路，给出必要的数学推导，便于读者掌握统计学习方法的实质，学会运用。
《统计学习方法》可以说是机器学习的入门宝典，许多机器学习培训班、互联网企业的面试、笔试题目，很多都参考这本书。
今天我们将李航老师的《统计学习方法》第二版的代码进行了整理，并提供下载。
非常感谢各位朋友贡献的自己的笔记、代码！
2020年6月7日
代码目录
第1章 统计学习方法概论
第2章 感知机
第3章 k近邻法
第4章 朴素贝叶斯
第5章 决策树
第6章 逻辑斯谛回归
第7章 支持向量机
第8章 提升方法
第9章 EM算法及其推广
...
地址：https://github.com/fengdu78/lihang-code
🤩Python随身听-技术精选： /wesm/pydata-book
👉Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
😎TOPICS: ``
⭐️STARS：13529, 今日上升数↑:13
👉README：
Python for Data Analysis, 2nd Edition
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney,
published by O'Reilly Media
[Buy the book on Amazon][1]
1st Edition Readers
If you are reading the [1st Edition][1] (published in 2012), please find the
reorganized book materials on the [
1st-editionbranch][2].Translations
IPython Notebooks:
地址：https://github.com/wesm/pydata-book
🤩Python随身听-技术精选： /AliaksandrSiarohin/first-order-model
👉This repository contains the source code for the paper First Order Motion Model for Image Animation
😎TOPICS:
deep-learning,image-animation,generative-model,motion-retargeting⭐️STARS：8155, 今日上升数↑:20
👉README：
First Order Motion Model for Image Animation
This repository contains the source code for the paper First Order Motion Model for Image Animation by Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci and Nicu Sebe.
Example animations
The videos on the left show the driving videos. The first row on the right for each dataset shows the source videos. The bottom row contains the animated sequences with motion transferred from the driving video and object taken from the source image. We trained a separate network for each task.
VoxCeleb Dataset
Fashion Dataset
MGIF Dataset
Installation
We support
python3. To install the dependencies run:pip install -r requirements.txt
YAML configs
There are several configuration (
config/dataset_name.yaml) files one for each `da...地址：https://github.com/AliaksandrSiarohin/first-order-model
🤩Python随身听-技术精选： /hardikkamboj/An-Introduction-to-Statistical-Learning
👉This repository contains the exercises and its solution contained in the book An Introduction to Statistical Learning
😎TOPICS:
datascience,machine-learning,statistical-learning,python⭐️STARS：501, 今日上升数↑:116
👉README：
An-Introduction-to-Statistical-Learning
This repository contains the exercises and its solution contained in the book An Introduction to Statistical Learning
An-Introduction-to-Statistical-Learning is one of the most popular books among data scientists to learn the conepts and intuitions behind
machine learning algorithms, however, the exercises are implemented in R language, which is a hinderence for all those who are using python
language. To overcome this i have tried solving all the questions in practical exerices in Python language, so people using python language
can also get the most our of this amazing book. Along with that i have also provided the solutions for conceptual questions.
I had tried my best to write the correct solutions to the problem, It was a challenge, and i need to learn to do a lot of research. I do not gurantee that all the solutions are
absoletely correct. I have commented the notebooks.
If you find any query, do send a fe...
地址：https://github.com/hardikkamboj/An-Introduction-to-Statistical-Learning
🤩Python随身听-技术精选： /fastai/fastbook
👉The fastai book, published as Jupyter Notebooks
😎TOPICS:
notebooks,fastai,deep-learning,machine-learning,data-science,python,book⭐️STARS：9756, 今日上升数↑:19
👉README：
English / Spanish / Korean / Chinese /
Bengali
The fastai book
These notebooks cover an introduction to deep learning, fastai, and PyTorch. fastai is a layered API for deep learning; for more information, see the fastai paper. Everything in this repo is copyright Jeremy Howard and Sylvain Gugger, 2020 onwards.
These notebooks are used for a MOOC and form the basis of this book, which is currently available for purchase. It does not have the same GPL restrictions that are on this draft.
The code in the notebooks and python
.pyfiles is covered by the GPL v3 license; see the LICENSE file for details.The remainder (including all markdown cells in the notebooks and other prose) is not licensed for any redistribution or change ...
地址：https://github.com/fastai/fastbook
The text was updated successfully, but these errors were encountered: