A collection of (mostly) technical things every software developer should know about

This repository is to prepare for Machine Learning interviews.

A collection of learning resources for curious software engineers

Data Analysis course notebooks in R

Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

Конспект лекций по курсу "Основы статистики"

Multi-armed bandit problem under delayed feedback: framework for the numerical experiments

Data science interview questions with answers. Not ideally (yet)

Data science interview questions and answers

Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.

A tour through recommendation algorithms in python [IN PROGRESS]

😱 Falsehoods Programmers Believe in

Resilience engineering papers

The Patterns of Scalable, Reliable, and Performant Large-Scale Systems

A curated list of awesome big data frameworks, ressources and other awesomeness.

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

A curated list of data engineering tools for software developers

Notes talking about the design and implementation of Apache Spark

Time Tracking Extension for Chrome Web Store

Implementation of the Circuit Breaker in Go based on sync/atomic

A complete daily plan for studying to become a machine learning engineer.

Leader Election Implementation

Basic Algorithms written in Ruby

Yet Another Try Ruby

Google Analytics Export API Ruby Wrapper

An example of the reverse pagination

Example of the reverse pagination

Generation a friendly token randomically.