Advanced Statistics and Predictions API for My Final Project

The Decision Tree Model Trainer Backend is a server-side application built with Node.js and Python. It is designed to handle the training of decision tree models based on user input.

Repository showcasing a Convolutional Neural Network (CNN) optimized for parallel execution on GPU using cuDNN, with comprehensive scripts for dataset distribution, model training, validation, and testing.

Elile AI Intern Kaggle Competition | Part 1

A template for training neural networks by leveraging parallel computation on multiple GPUs, real time metrics monitoring using Tensorboard, and more.

Analyzing and predicting Google's stock prices through detailed data exploration and advanced LSTM models. This project involves data preprocessing, creating time-series sequences, constructing and training LSTM networks, and evaluating their performance to forecast future stock prices utilizing Python and Machine Learning libraries.

Give your computer an AI Brain

An AI-driven solution for enhancing safety at construction sites. Utilises YOLOv8 for object detection to identify overhead hazards like heavy loads and steel pipes. Alerts are triggered if personnel are detected beneath these hazards. Dataset sourced from Taiwan's construction industry.

This project involves a comprehensive comparative analysis of various machine learning models to classify activities based on a given dataset. The analysis follows a structured approach, including data exploration, model training, model evaluation, and results interpretation to identify the best performing model.

This project is a web application for predicting loan approval status based on various financial and personal attributes. It uses a machine learning model that I trained on historical loan data to make predictions. I built the web application using Flask for the web framework, SQLite for the database, and the pre-trained model saved with joblib.

Resource scheduling and cluster management for AI

This project involves training a machine learning model and plotting its learning curves to analyze training and testing accuracies, utilizing Java for model execution and Python for data visualization. It includes commands for compiling and running the Java program, generating plots, and sending results via email.

Notebooks for detection and classification model training. Insect classification model. Python scripts for processing of data, collected with the Insect Detect DIY camera trap.

🤖 DeepCaptcha Solver 🚀 | A cutting-edge deep learning model for solving CAPTCHA images with high accuracy using CNNs and Keras. 🌟

🐾 Training a machine learning model to recognize 15 different animal classes and classify images accordingly.

Simpler Distil-Whisper

An ML model trained on MNIST dataset to classify handwritten digits correctly.

A machine learning project for detecting fraudulent transactions in fintech banking systems. Includes data preprocessing, feature engineering, and model evaluation.

Explore a variety of R-based data visualizations and models in this repository. Curated and crafted by a data enthusiast, these resources showcase the versatility of R in analytics and modeling.

An image recognizer that recognizes 20 different shapes of Pasta.