python forward是什么意思中文-Python随身听-2020-10-22-技术精选

致读者：亲爱的「Python随身听」的观众们，这是由DE8UG的人工非智能给你带来的新的一期技术精选。

主要为编程初学者，开发工程师，算法工程师，数据分析师，运维，测试，运营，产品等各个岗位的Python爱好者带来Python世界的流行趋势，前沿技术。

你可以挑选自己喜欢的项目尽情玩耍，任何想法欢迎留言讨论。

本文的结构和内容会经常更新，每天10：24分左右发布，感谢订阅

�和收藏☆。

（点击原文或到pythonradio.online网站查看可点击的文档链接）

�Python随身听-技术精选：/dabeaz-course/practical-python

�Practical Python Programming (course by @dabeaz)

�TOPICS: ``

⭐️STARS：6369, 今日上升数↑:207

�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：/mlech26l/keras-ncp

�Code repository of the paper Neural circuit policies enabling auditable autonomy published in Nature Machine Intelligence

�TOPICS: ncp,recurrent-neural-network,nature-machine-intelligence,tensorflow,keras

⭐️STARS：366, 今日上升数↑:113

�README：

Neural Circuit Policies Enabling Auditable Autonomy

Neural Circuit Policies (NCPs) are designed sparse recurrent neural networks based on the LTC neuron and synapse model loosely inspired by the nervous system of the organism C. elegans.

This page is a description of the Keras (TensorFlow 2.0 package) reference implementation of NCPs.

For reproducibility materials of the paper see the corresponding subpage.

Installation

Requirements:

Python 3.6

TensorFlow 2.0

pip install keras-ncp

Colab notebooks

We have created a few Google Colab notebooks for an interactive introduction to the package

Google Colab: Basic usage

[Google Colab: Stacking NCPs with other layers](https://colab.research.google.com/drive/1-mZunxqVkfZVBXNPG0kTSKUNQUSdZiBI?usp=sharing...

地址：https://github.com/mlech26l/keras-ncp

�Python随身听-技术精选：/satwikkansal/wtfpython

�What the f*ck Python?

�TOPICS: python,wats,snippets,wtf,gotchas,documentation,pitfalls,interview-questions,python-interview-questions

⭐️STARS：21963, 今日上升数↑:64

�README：

What the f*ck Python!

�

Exploring and understanding Python through surprising snippets.

Translations: Chinese 中文 | Vietnamese Tiếng Việt | Add translation

Other modes: Interactive | CLI

Python, being a beautifully designed high-level and interpreter-based programming language, provides us with many features for the programmer's comfort. But sometimes, the outcomes of a Python snippet may not seem obvious at first sight.

Here's a fun project attempting to explain what exact...

地址：https://github.com/satwikkansal/wtfpython

�Python随身听-技术精选：/lhwolff15/InsiderTrading

�Looks up insider trading transactions in a date range

�TOPICS: ``

⭐️STARS：89, 今日上升数↑:21

�README：

Insider Trading

Looks up insider trading transactions in a date range

This program is designed to pull all the insider trading transactions within a specific date range from the Securities and Exchange Commission (SEC) website. Before running this code, you need to download the ticker_cik_edgar_cik.csv file that is included on the main page of this repository. This file contains all the tickers in the SEC EDGAR database along with their corresponding CIK number (how the SEC organizes their data for each company). Enter that file path into the pd.read_csv('') blank in Lines 10, 18, and 34. Lines 1-6 are the various imports needed to run this file. Lines 8-30 are additional functions I've created to ease the process. The first converts a symbol into a CIK number, the second function does the opposite of the first (CIK--->symbol), and the third function converts a url into a BeautifulSoup object.

Lines 33-108 contain the main data-gathering function. Right now, the function is set to gather insider trading...

地址：https://github.com/lhwolff15/InsiderTrading

�Python随身听-技术精选：/google/jax

�Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

�TOPICS: ``

⭐️STARS：9942, 今日上升数↑:11

�README：

Quickstart

| Transformations

| Install guide

| Change logs

| Reference docs

| Code search

News: JAX tops largest-scale MLPerf Training 0.7 benchmarks!

What is JAX?

JAX is Autograd and

XLA,

brought together for high-performance machine learning research.

With its updated version of Autograd,

JAX can automatically differentiate native

Python and NumPy functions. It can differentiate t...

地址：https://github.com/google/jax

�Python随身听-技术精选：/facebookresearch/detectron2

�Detectron2 is FAIR's next-generation platform for object detection and segmentation.

�TOPICS: ``

⭐️STARS：13148, 今日上升数↑:13

�README：

Detectron2 is Facebook AI Research's next generation software system

that implements state-of-the-art object detection algorithms.

It is a ground-up rewrite of the previous version,

Detectron,

and it originates from maskrcnn-benchmark.

What's New

It is powered by the PyTorch deep learning framework.

Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend,

DeepLab, etc.

Can be used as a library to support different projects on top of it.

We'll open source more research projects in this way.

It trains much faster.

See our [blog post](ht...

地址：https://github.com/facebookresearch/detectron2

�Python随身听-技术精选：/0voice/interview_internal_reference

�2020年最新总结，阿里，腾讯，百度，美团，头条等技术面试题目，以及答案，专家出题人分析汇总。

�TOPICS: ``

⭐️STARS：29161, 今日上升数↑:23

�README：

2020年最新总结，阿里，腾讯，百度，美团，头条等技术面试题目，以及答案，专家出题人分析汇总。持续更新中。

阿里篇

华为篇

百度篇

腾讯篇

美团篇

头条篇

滴滴篇

京东篇

MySQL篇

Redis篇

MongoDB篇

Zookeeper篇

Nginx篇

算法篇

内存篇

cpu篇

磁盘篇

网络通信篇

安全篇

并发篇

阿里篇

1.1.1 如何实现一个高效的单向链表逆序输出？

1.1.2 已知sqrt(2)约等于1.414，要求不用数学库，求sqrt(2)精确到小数点后10位

[1.1.3 给定一个二叉搜索树(BST)，找到树中第 K 小的节点](01.阿里篇/1.1.3%20%E7%BB%99%E5%AE%9A%E4%B8%80%E4%B8%AA%E4%BA%8C%E5%8F%89%E6%90%9C%E7%B4%A2%...

地址：https://github.com/0voice/interview_internal_reference

�Python随身听-技术精选：/neuml/tldrstory

�AI-powered understanding of headlines and story text

�TOPICS: ``

⭐️STARS：121, 今日上升数↑:19

�README：

tldrstory: AI-powered understanding of headlines and story text

tldrstory is a framework for AI-powered understanding of headlines and text content related to stories. tldrstory applies zero-shot labeling over text, which allows dynamically categorizing content. This framework also builds a txtai index that enables text similarity search. A customizable Streamlit application and FastAPI backend service allows users to review and analyze the data processed.

Examples

The following links are example applications built with tldrstory.

Election 2020 (Configuration files)

Mobile Tech News (Configuration files)

https://github.com/neuml/tldrstory

�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：699, 今日上升数↑:81

�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：/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：90142, 今日上升数↑:173

�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：/geohot/tinygrad

�You like pytorch? You like micrograd? You love tinygrad!

❤️

�TOPICS: ``

⭐️STARS：111, 今日上升数↑:19

�README：

tinygrad

For something in between a pytorch and a karpathy/micrograd

This may not be the best deep learning framework, but it is a deep learning framework.

The Tensor class is a wrapper around a numpy array, except it does Tensor things.

Example

import numpy as np

from tinygrad.tensor import Tensor

x = Tensor(np.eye(3))

y = Tensor(np.array([[2.0,0,-2.0]]))

z = y.dot(x).sum()

z.backward()

print(x.grad) # dz/dx

print(y.grad) # dz/dy

Same example in torch

import torch

x = torch.eye(3, requires_grad=True)

y = torch.tensor([[2.0,0,-2.0]], requires_grad=True)

z = y.matmul(x).sum()

z.backward()

print(x.grad) # dz/dx

print(y.grad) # dz/dy

Neural networks?

It turns out, a decent autograd tensor library is 90% of what you need for neural networks. Add an ...

地址：https://github.com/geohot/tinygrad

�Python随身听-技术精选：/ageitgey/face_recognition

�The world's simplest facial recognition api for Python and the command line

�TOPICS: machine-learning,face-detection,face-recognition,python

⭐️STARS：36768, 今日上升数↑:26

�README：

Face Recognition

You can also read a translated version of this file in Chinese 简体中文版 or in Korean 한국어 or in Japanese 日本語.

Recognize and manipulate faces from Python or from the command line with

the world's simplest face recognition library.

Built using dlib's state-of-the-art face recognition

built with deep learning. The model has an accuracy of 99.38% on the

Labeled Faces in the Wild benchmark.

This also provides a simple face_recognition command line tool that lets

you do face recognition on a folder of images from the command line!

Features

Find faces in pictures

Find all the faces that appear in a picture:

import face_recognition

image = face_recognition.load_image_file("your_file...

地址：https://github.com/ageitgey/face_recognition

�Python随身听-技术精选：/airbnb/knowledge-repo

�A next-generation curated knowledge sharing platform for data scientists and other technical professions.

�TOPICS: data,data-science,knowledge,data-analysis

⭐️STARS：4512, 今日上升数↑:12

�README：

Knowledge Repo

The Knowledge Repo project is focused on facilitating the sharing of

knowledge between data scientists and other technical roles using data formats

and tools that make sense in these professions. It provides various data stores

(and utilities to manage them) for "knowledge posts", with a particular focus on

notebooks (R Markdown and Jupyter / IPython Notebook) to better promote

reproducible res...

地址：https://github.com/airbnb/knowledge-repo

�Python随身听-技术精选：/open-mmlab/mmdetection

�OpenMMLab Detection Toolbox and Benchmark

�TOPICS: object-detection,instance-segmentation,rpn,fast-rcnn,faster-rcnn,mask-rcnn,cascade-rcnn,ssd,retinanet,pytorch

⭐️STARS：12151, 今日上升数↑:22

�README：

News: We released the technical report on ArXiv.

Documentation: https://mmdetection.readthedocs.io/

Introduction

MMDetection is an open source object detection toolbox based on PyTorch. It is

a part of the OpenMMLab project developed by Multimedia Laboratory, CUHK.

The master branch works with PyTorch 1.3 to 1.6.

The old v1.x branch works with PyTorch 1.1 to 1.4, but v2.0 is strongly recommended for faster speed, higher performance, better design and more friendly usage.

Major features

Modular Design

We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules.

Support of multiple frameworks out of box

The toolbox directly supports popular and contemporary detection frameworks, e.g. Faster RCNN, Mask RCNN, RetinaNet, e...

地址：https://github.com/open-mmlab/mmdetection

�Python随身听-技术精选：/Jack-Cherish/python-spider

�

�Python3网络爬虫实战：淘宝、京东、网易云、B站、12306、抖音、笔趣阁、漫画小说下载、音乐电影下载等

�TOPICS: python-spider,python3,python,webspider

⭐️STARS：11535, 今日上升数↑:19

�README：

注：2020年最新连载教程请移步：Python Spider 2020

Python Spider

原创文章每周最少两篇，后续最新文章会在【公众号】首发，视频【B站】首发，大家可以加我【微信】进交流群，技术交流或提意见都可以，欢迎Star！

地址：https://github.com/Jack-Cherish/python-spider

�Python随身听-技术精选：/localstack/localstack

�

� A fully functional local AWS cloud stack. Develop and test your cloud & Serverless apps offline!

�TOPICS: aws

⭐️STARS：26899, 今日上升数↑:17

�README：

LocalStack - A fully functional local AWS cloud stack

LocalStack provides an easy-to-use test/mocking framework for developing Cloud applications.

Currently, the focus is primarily on supporting the AWS cloud stack.

Announcements

2020-09-15: A major (breaking) change has been merged in PR #2905 - starting with releases after v0.11.5, all services are now exposed via the edge service (port 4566) only! Please update your client configurations to use this new endpoint.

2019-10-09: LocalStack Pro is out! We're incredibly excited to announce the launch of LocalStack Pro - the enterprise version of LocalStack with additional APIs and advanced features. Check out the free trial at https://localstack.cloud

2018-01-10: Help wanted! Please fill out this survey to support a research study on the usage of Serverless and Function-as-a-Service (FaaS) services, conducted at the Chalmers University of Technology. ...

地址：https://github.com/localstack/localstack

�Python随身听-技术精选：/doccano/doccano

�Open source text annotation tool for machine learning practitioner.

�TOPICS: natural-language-processing,machine-learning,active-learning,annotation-tool,python,datasets,dataset,data-labeling,text-annotation

⭐️STARS：3720, 今日上升数↑:17

�README：

doccano

doccano is an open source text annotation tool for humans. It provides annotation features for text classification, sequence labeling and sequence to sequence tasks. So, you can create labeled data for sentiment analysis, named entity recognition, text summarization and so on. Just create a project, upload data and start annotating. You can build a dataset in hours.

Demo

You can try the annotation demo.

Features

Collaborative annotation

Multi-language support

Mobile support

Emoji

�

support

Dark theme

RESTful API

Usage

Two options to run doccano:

production,

development.

To use doccano, please follow:

Install dependencies

You need to install dependencies:

Git,

Docker,

Docker Compose.

Get the code

You need to clone the repository:

$ git clone https...

地址：https://github.com/doccano/doccano

�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：109816, 今日上升数↑:64

�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：/tiangolo/fastapi

�FastAPI framework, high performance, easy to learn, fast to code, ready for production

�TOPICS: python,json,swagger-ui,redoc,starlette,openapi,api,openapi3,framework,async,asyncio,uvicorn,python3,python-types,pydantic,json-schema,fastapi,swagger,rest,web

⭐️STARS：22224, 今日上升数↑:43

�README：

FastAPI framework, high performance, easy to learn, fast to code, ready for production

https://github.com/tiangolo/fastapi

�Python随身听-技术精选：/MicrosoftDocs/ml-basics

�Exercise notebooks for Machine Learning modules on Microsoft Learn

�TOPICS: ``

⭐️STARS：425, 今日上升数↑:76

�README：

Machine Learning Basics

This repository contains the exercise files for the [Create machine learning models](https://docs.microsoft.com/learn/paths/create-machine-learn-models...

地址：https://github.com/MicrosoftDocs/ml-basics

�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 in python.

�TOPICS: datascience,machine-learning,statistical-learning,python

⭐️STARS：569, 今日上升数↑:42

�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：/Pierian-Data/Complete-Python-3-Bootcamp

�Course Files for Complete Python 3 Bootcamp Course on Udemy

�TOPICS: ``

⭐️STARS：12545, 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：/slundberg/shap

�A game theoretic approach to explain the output of any machine learning model.

�TOPICS: interpretability,machine-learning,deep-learning,gradient-boosting,shap,shapley,explainability

⭐️STARS：10535, 今日上升数↑:18

�README：

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations).

Install

Shap can be installed from either ...

地址：https://github.com/slundberg/shap

�Python随身听-技术精选：/Mikoto10032/DeepLearning

�深度学习入门教程, 优秀文章, Deep Learning Tutorial

�TOPICS: deeplearning,cnn,rnn,gan,machine-learning,tensorflow,mxnet,deep-learning,machinelearning,pytorch,gcn,kaggle

⭐️STARS：3856, 今日上升数↑:11

�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

矩阵微积分

机器学习的数学基础

CS229线性代数与概率论基础

机器学习基础

快速入门

机器学习算法地图

机器学习 吴恩达 Coursera个人笔记 && 视频（含官方笔记）

[CS229 课程讲义中文翻译](https://kivy-cn.gith...

地址：https://github.com/Mikoto10032/DeepLearning

�Python随身听-技术精选：/ShusenTang/Dive-into-DL-PyTorch

�本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

�TOPICS: deep-learning,deep-learning-tutorial,pytorch,pytorch-tutorial,computer-vision,natural-language-processing,d2l

⭐️STARS：10838, 今日上升数↑:20

�README：

本项目将《动手学深度学习》 原书中MXNet代码实现改为PyTorch实现。原书作者：阿斯顿·张、李沐、扎卡里 C. 立顿、亚历山大 J. 斯莫拉以及其他社区贡献者，GitHub地址：https://github.com/d2l-ai/d2l-zh

此书的中英版本存在一些不同，针对此书英文版的PyTorch重构可参考这个项目。

There are some differences between the Chinese and English versions of this book. For the PyTorch modifying of the English version, you can refer to this repo.

简介

本仓库主要包含code和docs两个文件夹（外加一些数据存放在data中）。其中code文件夹就是每章相关jupyter notebook代码（基于PyTorch）；docs文件夹就是markdown格式的《动手学深度学习》书中的相关内容，然后利用docsify将网页文档部署到GitHub Pages上，由于原书使用的是MXNet框架，所以docs内容可能与原书略有不同，但是整体内容是一样的。欢迎对本项目做出贡献或提出issue。

面向人群

本项目面向对深度学习感兴趣，尤其是想使用PyTorch进行深度学习的童鞋。本项目并不要求你有任何深度学习或者机器学习的背景知识，你只需了解基础的数学和编程，如基础的线性代数、微分和概率，以及基础的Python编程。

食用方法...

地址：https://github.com/ShusenTang/Dive-into-DL-PyTorch

�Python随身听-技术精选：/CoreyMSchafer/code_snippets

�None

�TOPICS: ``

⭐️STARS：5926, 今日上升数↑:12

�README：

code_...

地址：https://github.com/CoreyMSchafer/code_snippets

�Python随身听-技术精选：/MicrosoftLearning/DP100

�Labs for Course DP-100: Designing and Implementing Data Science Solutions on Microsoft Azure

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DP-100: Designing and Implementing a Data Science Solution on Azure

This repo contains the lab files for Microsoft course [DP-100T01-A: Designing and Implementing a Data Science Solution on Azure](https://docs.microsoft.com/en-us/learn/certifications/courses/dp-100t01...

地址：https://github.com/MicrosoftLearning/DP100