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Showing 9 open source projects for "reference"
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1
The Algorithms Python
All Algorithms implemented in Python
...The project covers various domains including mathematics, cryptography, machine learning, sorting, graph theory, and more. With contributions from a large global community, it continually grows and improves through collaboration and peer review. This repository is an ideal reference for students, educators, and developers seeking hands-on experience with algorithmic concepts in Python. -
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X's Recommendation Algorithm
Source code for the X Recommendation Algorithm
...While certain components (such as safety layers, spam detection, or private data) are excluded, the release provides valuable insights into the design of real-world machine learning–driven ranking systems. The project is intended as a reference for researchers, developers, and the public to study, experiment with, and better understand the mechanisms behind social media content. -
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Pythonic Data Structures and Algorithms
Minimal examples of data structures and algorithms in Python
...It offers working, often well-commented code for many standard algorithmic problems — from sorting/searching to graph algorithms, dynamic programming, data structures, and more — making it a valuable resource for learning and reference. For students preparing for technical interviews, self-learners brushing up on fundamentals, or developers wanting to understand algorithm internals, this repository provides ready-to-run examples, and can serve as a sandbox to experiment, benchmark, or adapt code. Because it’s in pure Python, it’s easy to read and modify, making it accessible even to those with modest programming experience. ... -
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interactive-coding-challenges
120+ interactive Python coding interview challenges
...Problems span arrays, strings, stacks, queues, linked lists, trees, graphs, dynamic programming, and more, mirroring common interview themes. Many challenges include hints and reference solutions so you can compare approaches and learn idiomatic patterns. The structure encourages incremental improvement—start with a brute-force idea, then refine to optimal time and space complexity. It serves both as a self-study path and as a warm-up bank for interview prep or coding katas. -
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5
MADDPG
Code for the MADDPG algorithm from a paper
MADDPG (Multi-Agent Deep Deterministic Policy Gradient) is the official code release from OpenAI’s paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. The repository implements a multi-agent reinforcement learning algorithm that extends DDPG to scenarios where multiple agents interact in shared environments. Each agent has its own policy, but training uses centralized critics conditioned on the observations and actions of all agents, enabling learning in... -
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Baselines
High-quality implementations of reinforcement learning algorithms
Unlike the other two, openai/baselines is not currently a maintained or prominent repo in the OpenAI organization (and I found no strong reference in OpenAI’s main GitHub). Historically, “baselines” repositories are often used for baseline implementations of reinforcement learning algorithms or reference models (e.g. in the RL domain). If there was an OpenAI “baselines” repo, it might have contained reference implementations for reinforcement learning or model policy baselines to compare new work against. ... -
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Algorithms in Python
Data Structures and Algorithms in Python
Algorithms in Python is a collection of algorithm and data structure implementations (primarily in Python) meant to serve as both learning material and reference code for engineers. It includes code for graph algorithms, heap data structures, stacks, queues, and more — each implemented cleanly so learners can trace logic and adapt for their problems. The repository is particularly useful for people preparing for competitive programming, job interviews, or building a foundational understanding of algorithmic patterns. ... -
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The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.
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9
pydstruct
Provide Reference Implementations of Data Structures in Python(3)
The aim of the project is to provide reference implementations of data structures along with supporting references to publications and other materials. The only purpose is for fun and education: I like data structures. -
Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
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