Essays

- [Disclaimer](#disclaimer) - [The Holy Quaternity](#the-holy-quaternity) - [Core](#core) - [Beginner](#beginner) - [Advanced](#advanced) - [Testing](#testing) - [Pytest](#pytest) - [Data](#data) - [Jupyter](#jupyter) - [Machine Learning](#machine-learning) - [Keras](#keras) - [TensorFlow](#tensorflow) - [Pytorch](#pytorch) - [Gluon](#gluon) - [Natural Language Processing](#natural-language-processing) - [Data Pipelines & Distributed Programming](#data-pipelines--distributed-programming) - [Airflow](#airflow) - [Beam](#beam) - [Dask](#dask) - [Statistic & Probabilistic Programming](#statistic--probabilistic-programming) - [Visualization](#visualization) - [Python Libraries](#python-libraries) - [Bokeh](#bokeh) - [Plotly & Dash](#plotly--dash) - [Javascript Libraries](#javascript-libraries) - [Functional Programming](#functional-programming) - [Coconut](#coconut) - [Genomics](#genomics) - [Web Development](#web-development) - [Web Frameworks](#web-frameworks) - [Static Sites](#static-sites) - [Pelican](#pelican)

Disclaimer

Scattered across the Net, there's an enormous wealth of Pythonic knowledge. While there already exist places where such information is aggregated, here, I am humbly offering my own personal take on such effort. This is not a complete list by any means as I cannot cover the vastness of the Python ecosystem with my experience however, what I can do, is take you with me alongside my maze of bookmarks, articles, books, talks and whatnot, you may not see the bigger picture from here, but I can assure you, will for certain find precise domain-specific references for what I regard as high quality educational material.