Repositories

Repositories

1. Battle Game

Repo Description: A simple 2D battle game developed in Python using the Pygame library. Players engage in real-time combat against each other, utilizing different weapons and abilities. The game supports 1vs1 PVP mode with player movement, attack controls, and health/energy management.

Key Features:

  • Real-time 1vs1 battles
  • Health and energy management
  • Interactive environment and effects
  • Easy-to-use controls via keyboard

Setup & Run:

  1. Clone and install dependencies via pip.
  2. Run the game script or compile it into an executable with PyInstaller.

Link: Battle Game Repo

2. Music Genre Classification

Repo Description: This project focuses on music genre classification using machine learning techniques. It involves the training and deployment of a pre-trained audio model fine-tuned on the GTZAN dataset, utilizing the HuggingFace platform for model deployment.

Key Features:

  • Music genre classification with pre-trained models
  • Utilizes HuggingFace for model training and inference
  • Data preprocessing, feature extraction, and model evaluation pipelines

Setup & Run:

  1. Set up both Conda and virtualenv environments.
  2. Train models with the provided Python scripts and run them using custom pipelines.

Link: Music Genre Classification Repo

3. MLOps Challenge

Repo Description: This project implements an end-to-end ML solution to classify keystrokes, serving the trained models via a REST API. It focuses on building a reliable data pipeline, model training, and deployment in a production environment.

Key Features:

  • Data processing and feature extraction pipeline
  • Model training and evaluation for keystroke classification
  • REST API for inference serving
  • Deployed API on AWS EC2 instance for public access

Setup & Run:

  1. Set up both development and production environments.
  2. Train models and serve them via a REST API using FastAPI and Uvicorn.

Link: MLOps Challenge Repo

4. LLM QA Retrieval

Repo Description: This project involves building a QA retrieval system using Large Language Models (LLMs). It includes zero-shot and few-shot learning techniques to answer questions from fetched news data, employing ChromaDB for semantic search and text generation using LLMs.

Key Features:

  • Fetches news articles and processes them for semantic search
  • Implements a QA pipeline with text generation using LLMs
  • Configurable pipeline for different query types and news sources
  • Integration with Weights & Biases for model tracking and reporting

Setup & Run:

  1. Fetch and chunk news articles, then run the QA pipeline for answering queries.
  2. Use pre-configured models for zero-shot or fine-tuned QA.

Link: LLM QA Retrieval Repo

5. LLM Text Generation

Repo Description: A project dedicated to exploring the text generation capabilities of Large Language Models (LLMs). It focuses on model fine-tuning, text generation tasks, and deploying these models for real-world applications.

Key Features:

  • Fine-tuning of large language models for text generation
  • Data preprocessing and pipeline management
  • Visualizations and evaluation of generated texts

Link: LLM Text Generation Repo

6. Activity Recognition

Repo Description: Machine Learning Challenge for Human Activity Recognition. This project is organized to facilitate data processing, model training, and evaluation for recognizing human activities based on sensor data.

Key Features:

  • Human Activity Recognition Data Analysis
  • Data preprocessing and Pipeline Management
  • Exploratory Data Analysis and Machine Learning Development
  • Visualizations and Final Presentation

Setup & Run:

  1. Set up both Conda and virtualenv environments.
  2. Train models and perform EDA with the provided Python scripts and run them using custom pipelines.

Link: HAR Prediction Repo