Popular GitHub Repositories for Bronze Silver Gold

Notes for the Reinforcement Learning course by David Silver along with implementation of various algorithms.

4th Place Solution for the Kaggle Competition: LMSYS - Chatbot Arena Human Preference Predictions

Data Forge — a modern data stack playground to practice flows and best practices, not just tools. Spark, Trino, Kafka, Iceberg, ClickHouse, Airflow, MinIO, Superset — all wired together locally with Docker Compose.

Data and evaluation code for the paper WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER (EMNLP 2021).

Gold Trading Bot Assistant | Deepseek | Langchain | Python | Gen AI

Original PyTorch implementation of the paper "Semantic Segmentation under Adverse Conditions: A Weather and Nighttime-aware Synthetic Data-based Approach", published at the British Machine Vision Conference (BMVC) 2022.

A Jax/Stax implementation of the general meta learning paper: Oh, J., Hessel, M., Czarnecki, W.M., Xu, Z., van Hasselt, H.P., Singh, S. and Silver, D., 2020. Discovering reinforcement learning algorithms. Advances in Neural Information Processing Systems, 33.

Revolutionary AI-powered ETL pipeline combining Medallion Architecture with autonomous agents on Databricks. Built during Accenture internship using LangChain/LangGraph for self-healing data transformations. Zero-touch Bronze→Silver→Gold processing with complete LangSmith observability.

? This repository features an intelligent agent that autonomously explores the Wumpus World—a grid filled with hidden Wumpuses, pits, and gold. Using logic, inference, and planning, the agent avoids dangers, finds gold, and escapes. Includes real-time Pygame visualization and customizable settings, ideal for AI/game logic study and experimentation

Navigate the dungeon, avoid the pits, find the gold, beware of the wumpus. Artificial intelligence based AI game.

A jax/stax implementation of: Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G. and Petersen, S., 2015. Human-level control through deep reinforcement learning. nature, 518(7540), pp.529-533.

Tiny QA Benchmark++ a micro-benchmark suite (52-item gold + on-demand multilingual synthetic packs), generator CLI, and CI-ready eval harness for ultra-fast LLM smoke-testing & regression-catching.

BeSTspeech T-T-S speech synthesizer speak window.

Original PyTorch implementation of the paper "Leveraging Synthetic Data to Learn Video Stabilization Under Adverse Conditions", published at the EEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2024.

:3rd_place_medal: (Bronze medal - 100th place - Top 7%) Repository for the "Jigsaw Multilingual Toxic Comment Classification" Kaggle competition.

? Predicting the future adjusted closing price of Gold ETF using machine learning! ?✨

MetaTrader EA that automates disciplined gold trading with configurable risk rules for steady daily profits,

This repo contains data and scripts for MLIPs for borophene on silver

Is Temporal Difference Learning the Gold Standard for Stitching in RL? Code repository for research paper.