**Programming (learning)**

• **Books**

  • [10 лучших книг по программированию по мнению Reddit
  • [3 лучших книги по объектно-ориентированному программированию
  • [5 отличных англоязычных книг по теоретическому Computer Science
  • [6 бесплатных книг по алгоритмам в программировании
  • [6.042J Complete course notes
  • [8 книг по компьютерным сетям
  • [Build your first app \| Android Developers
  • [Building Blocks for Theoretical Computer Science

• **Developer\'s Tools**

  • [(Tutorial) Web Scraping With Python: Beautiful Soup - DataCamp
  • [\| fastai
  • [10 Best CSS Frameworks For Frontend Developers in 2020 - GeeksforGeeks
  • [10 лучших материалов для изучения Django
  • [20 short tutorials all data scientists should read (and practice) - Data Science Central
  • [7 Steps to Mastering Machine Learning With Python
  • [A Beginner's Introduction to Python Web Frameworks : Python
  • [A successful Git branching model » nvie.com
  • [ageitgey/face\_recognition: The world\'s simplest facial recognition api for Python and the command line
  • [Algorithms - Algorithmia
  • [All Tools --- PyViz 0.0.1 documentation
  • [An Intro to Git and GitHub for Beginners (Tutorial)
  • [Apache против Nginx: практические соображения
  • [ARCore - Google Developers
  • [Are you still using Pandas for big data? - Towards Data Science
  • [ArtVk & Bugtrack: Задачи по базам данных. Решение задач по SQL \[1\
  • [Awesome Python \| LibHunt
  • [awesome-vscode \| 🎨 A curated list of delightful VS Code packages and resources.
  • [Azure Machine Learning SDK for Python - Azure Machine Learning Python \| Microsoft Docs
  • [Batch convert images to PDF with Python by using Pillow or img2pdf \| Solarian Programmer
  • [benfred/py-spy: Sampling profiler for Python programs
  • [Better Code Hub
  • [Big data sets available for free - Data Science Central
  • [Build and deploy your first machine learning web app
  • [Build and run a Python app in a container
  • [Building A Blog Application With Django \| Django Central
  • [Building a Microservice in Python - Sonu Sharma - Medium
  • [Caffe \| Installation
  • [CAL board - Agile board - Jira
  • [Catalog of Patterns of Enterprise Application Architecture
  • [Chapter 1. Getting to know Redis - Redis in Action
  • [Choosing the right estimator --- scikit-learn 0.22.2 documentation
  • [CLI Setup - NativeScript Docs
  • [Cloud Shell
  • [Codacy Onboarding
  • [Codecov
  • [CodePen: Build, Test, and Discover Front-end Code.
  • [colorama · PyPI
  • [Conda Cheat Sheet - Kapeli
  • [Control Room \| Home \| Automation Anywhere
  • [Coveralls - Test Coverage History & Statistics
  • [CRAN - Package rattle
  • [create-graphql-server --- instantly scaffold a GraphQL server
  • [Dash for Beginners - DataCamp
  • [Dashboard : skromnitsky : PythonAnywhere
  • [Dask + Numba for Efficient In-Memory Model Scoring - Capital One Tech - Medium
  • [Data Science with Python: Intro to Data Visualization with Matplotlib
  • [Data: Querying, Analyzing and Downloading: The GDELT Project
  • [Dataset Search
  • [Datasets for Data Mining and Data Science
  • [Django ORM. Добавим сахарку / Хабр
  • [Django в примерах · GitBook (Legacy)
  • [Django на русском
  • [Django Руководство часть 11: Разворачивание сайта на сервере - Изучение веб-разработки \| MDN
  • [Download · Bootstrap
  • [Download All Free Textbooks from Springer using Python
  • [Download jQuery \| jQuery
  • [Download RequireJS
  • [ekzhu/datasketch: MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble
  • [emmetio/emmet-atom: Emmet support for Atom
  • [Erotemic/ubelt: A Python utility belt containing simple tools, a stdlib like feel, and extra batteries. Hashing, Caching, Timing, Progress, and more made easy!
  • [Exploring your data with just 1 line of Python - Towards Data Science
  • [eyaltrabelsi/pandas-log: The goal of pandas-log is to provide feedback about basic pandas operations. It provides simple wrapper functions for the most common functions that add additional logs
  • [facebook/create-react-app: Set up a modern web app by running one command.
  • [facebookresearch/detectron2: Detectron2 is FAIR\'s next-generation platform for object detection and segmentation.
  • [facebookresearch/DrQA: Reading Wikipedia to Answer Open-Domain Questions
  • [facebookresearch/faiss: A library for efficient similarity search and clustering of dense vectors.
  • [facebookresearch/fastMRI: A large-scale dataset of both raw MRI measurements and clinical MRI images
  • [facebookresearch/LAMA: LAnguage Model Analysis
  • [facebookresearch/nevergrad: A Python toolbox for performing gradient-free optimization
  • [facebookresearch/pytext: A natural language modeling framework based on PyTorch
  • [facebookresearch/pytorch\_GAN\_zoo: A mix of GAN implementations including progressive growing
  • [facebookresearch/VideoPose3D: Efficient 3D human pose estimation in video using 2D keypoint trajectories
  • [facebookresearch/vizseq: An Analysis Toolkit for Natural Language Generation (Translation, Captioning, Summarization, etc.)
  • [Getting started --- Flexx 1.0 documentation
  • [Getting started --- HiPlot 0.1.9.post2 documentation
  • [Getting started --- pandas 1.0.3 documentation
  • [Getting started --- SciPy.org
  • [Getting started with Django \| Django
  • [Getting started with PyMC3 --- PyMC3 3.8 documentation
  • [github/hub: A command-line tool that makes git easier to use with GitHub.
  • [Graphene-Python
  • [great-expectations/great\_expectations: Always know what to expect from your data.
  • [gto76/python-cheatsheet: Comprehensive Python Cheatsheet
  • [GUI (графический интерфейс пользователя) \| Python 3 для начинающих и чайников
  • [GuiProgramming - Python Wiki
  • [Hello, App Center
  • [How to deploy ML models using Flask + Gunicorn + Nginx + Docker
  • [How to Get a Job with Python - Towards Data Science
  • [How to Install and Run Hadoop on Windows for Beginners - Data Science Central
  • [How to Update All Python Packages \| ActiveState
  • [Installation --- Kivy 1.11.1 documentation
  • [Installation --- pyglet v1.5.0
  • [Integrating Summernote WYSIWYG Editor in Django \| Django Central
  • [interpretml/interpret: Fit interpretable machine learning models. Explain blackbox machine learning.
  • [Introducing Bamboolib --- a GUI for Pandas - Towards Data Science
  • [Introducing GitFlow
  • [ipinfo.io/json
  • [ironmussa/Optimus at develop-3.0
  • [japronto/1\_hello.md at master · squeaky-pl/japronto
  • [jaybaird/python-bloomfilter: Scalable Bloom Filter implemented in Python
  • [jazzband/pip-tools: A set of tools to keep your pinned Python dependencies fresh.
  • [Jetware - aise / tensorflow18\_keras21\_python36\_cpu\_notebook - 180509 appliance
  • [jiffyclub/snakeviz: An in-browser Python profile viewer
  • [JS Bin - Collaborative JavaScript Debugging
  • [Keras Cheat Sheet: Neural Networks in Python - DataCamp
  • [keyboard-shortcuts-windows.pdf
  • [knockknock/README.md at master · huggingface/knockknock
  • [Laravel Nova - Beautifully-designed administration panel for Laravel
  • [localhost:54321/callback?code=b7e3a07c1695c5b58b6d
  • [Machine Learning
  • [main
  • [marcotcr/lime: Lime: Explaining the predictions of any machine learning classifier
  • [MIT App Inventor 2
  • [More Itertools --- more-itertools 8.2.0 documentation
  • [nicolaskruchten/pivottable: Open-source Javascript Pivot Table (aka Pivot Grid, Pivot Chart, Cross-Tab) implementation with drag\'n\'drop.
  • [NLTK Data
  • [NoSQL Databases List by Hosting Data - Updated 2020
  • [Numba: A High Performance Python Compiler
  • [Numpy and Scipy Documentation --- Numpy and Scipy documentation
  • [NumPy в Python. Часть 1 / Хабр
  • [Optimus/README.md at master · ironmussa/Optimus
  • [Over 150 of the Best Machine Learning, NLP, and Python Tutorials I've Found
  • [Overview --- NumPy v1.19.dev0 Manual
  • [PageSpeed Insights
  • [pallets/werkzeug: The comprehensive WSGI web application library.
  • [Pivot Demo From Local CSV
  • [PivotTable.js
  • [Plotting Google Sheets data in Python with Folium - Towards Data Science
  • [Plugin Status · WakaTime
  • [Popper - Tooltip & Popover Positioning Engine
  • [PostgreSQL : Документация : Компания Postgres Professional
  • [Prisma - Database tools for modern application development
  • [Projects - Home
  • [pydqc/README.md at master · SauceCat/pydqc
  • [PyQt5 book with a foreword by the creator of PyQt
  • [Pythia's Documentation --- Pythia 0.3 documentation
  • [Python 3.8 documentation --- DevDocs
  • [Python Extension Packages for Windows - Christoph Gohlke
  • [Python Frameworks Comparison: How to Choose the Best for Web Development
  • [Python в три ручья: работаем с потоками (часть 1) \| GeekBrains - образовательный портал
  • [pytorch3d/INSTALL.md at master · facebookresearch/pytorch3d
  • [Qt Designer Download for Windows and Mac
  • [Quick Start \| GatsbyJS
  • [Quick Start · gulp.js
  • [Quickstart for Python/WSGI applications --- uWSGI 2.0 documentation
  • [Quickstart tutorial --- NumPy v1.19.dev0 Manual
  • [R Packages - RStudio
  • [RaRe-Technologies/bounter: Efficient Counter that uses a limited (bounded) amount of memory regardless of data size.
  • [React -- JavaScript-бібліотека для створення користувацьких інтерфейсів
  • [Roundup of Python NLP Libraries - NLP-FOR-HACKERS
  • [samuelhwilliams/Eel: A little Python library for making simple Electron-like HTML/JS GUI apps
  • [SciPy --- SciPy v1.4.1 Reference Guide
  • [SciPy.org --- SciPy.org
  • [Settings \| Account · WakaTime
  • [shaypal5/cachier: Persistent, stale-free, local and cross-machine caching for Python functions.
  • [sindresorhus/awesome: 😎 Awesome lists about all kinds of interesting topics
  • [skromnitsky/My\_Projects
  • [Speech Recognition - Speech to Text in Python using Google API, Wit.AI, IBM, CMUSphinx
  • [Speed Up your Algorithms Part 2--- Numba - Towards Data Science
  • [Speeding up your Algorithms Part 4--- Dask - Towards Data Science
  • [Stop Worrying and Create your Deep Learning Server in 30 minutes
  • [streamlit/streamlit: Streamlit --- The fastest way to build custom ML tools
  • [Superbird11/ranges: Continuous Range, RangeSet, and RangeDict data structures for Python
  • [The 30 Best Python Libraries and Packages for Beginners
  • [The Basics of Data Visualisation with Python - Towards Data Science
  • [The Big Bad NLP Database: Access Nearly 300 Datasets
  • [The Coolest Data Science And Machine Learning Tool Companies Of The 2020 Big Data 100
  • [The Most Underrated Python Packages - Towards Data Science
  • [The Super Duper NLP Repo: 100 Ready-to-Run Colab Notebooks
  • [Tkinter. Программирование GUI на Python. Курс
  • [Top Python Libraries Used In Data Science - Towards Data Science
  • [tqdm/tqdm: A Fast, Extensible Progress Bar for Python and CLI
  • [Travis CI
  • [Travis CI - Test and Deploy with Confidence
  • [Tutorial --- ZODB documentation
  • [Tutorial: Web Scraping in R with rvest -- Dataquest
  • [ucg8j/awesome-dash: A curated list of awesome Dash (plotly) resources
  • [umdjs/umd: UMD (Universal Module Definition) patterns for JavaScript modules that work everywhere.
  • [Untitled Diagram - diagrams.net
  • [Untitled Document - Creately
  • [Usage · s0md3v/XSStrike Wiki
  • [User\'s Guide --- Matplotlib 3.2.1 documentation
  • [vaexio/vaex: Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second 🚀
  • [vinta/awesome-python: A curated list of awesome Python frameworks, libraries, software and resources
  • [vstinner/pyperf: Toolkit to run Python benchmarks
  • [W3Schools Online Web Tutorials
  • [WAVE Report of Мої книги \| KUPRIENKO
  • [Web technology for developers \| MDN
  • [WebAssembly
  • [Website Style Guide Resources
  • [Welcome to pyjanitor's documentation! --- pyjanitor documentation