Popular GitHub Repositories for Rock Climbing

Tegridy MIDI Dataset for precise and effective Music AI models creation.

Can a neural network predict your next move on a game of rock, paper and scissor?

A Rock-Paper-Scissors game using computer vision and machine learning on Raspberry Pi

Mistral + Haystack: build RAG pipelines that rock ?

Training a Rock Paper Scissors model in the browser via TFJS - Learn along style

Using Neural Networks to generate new rock climbs for the moon board

Command-line application to produce generative art based on a target image

ROCK model for Knowledge-Based VQA in Videos

This app recognises 3 hand signs - fist, high five and victory hand [ rock, paper, scissors basically :) ] with live feed camera. It uses a HandSigns.mlmodel which has been trained using Custom Vision from Microsoft.

Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction

Generating English Rock lyrics using BERT

Using Simulated Annealing, Hill Climbing, and Genetic Algorithms to recreate images using shapes ? ?

? Computer Vision Games in Python! Hill climbing racing with hand detection, stack builder, pin pong ball, and dino game. Explore immersive gameplay with cutting-edge tech. Join me on this coding adventure! ?

A full stack python Flask artificial intelligence project capable of beating the human user in Rock Paper Scissors over 60% of the time using a custom scoring system to ensemble six models (na?ve logic-based, decision tree, neural network) trained on both game-level and stored historical data in AWS RDS Cloud SQL database.

Kaggle's Photorealistic images of the Moon's surface with ground truth rock segmentation.

Code and supplementary materials for the research paper titled 'Advancing Geological Image Segmentation: Deep Learning Approaches for Rock Type Identification and Classification', published in Applied Computing and Geosciences (Elsevier).

Proof-Of-Concept Piano-Drums Music AI Model/Implementation

Rock Music using Deep Learning

Play Rock, Paper, Scissors (Kaggle competition) with Reinforcement Learning: bandits, tabular Q-learning and PPO with LSTM.

This project explores the application of reinforcement learning (RL) to train humanoid robots for dynamic rock climbing movements, focusing on achieving the challenging "dyno" maneuver. Using the Proximal Policy Optimization (PPO) algorithm, the simulation integrates physics-based environments to model realistic climbing scenarios.