This repository contains C and C++ header files that include various standard library headers, providing essential functionality commonly used in C/C++ programming for a wide range of applications.
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Updated
Jul 7, 2024 - C++
This repository contains C and C++ header files that include various standard library headers, providing essential functionality commonly used in C/C++ programming for a wide range of applications.
Ceilidh20 /ˈkeɪ.li/, is a custom stream cipher cryptography inspired by traditional Scottish and Irish ceilidh dance, designed for secure encryption with enhanced randomization and high-entropy ciphertext outputs.
RiskMaster is an intelligent platform for individual investors to assess cryptocurrency market risks. It combines historical trend analysis and predictive neural networks - LSTM
a rate limiter project using nodejs and custom implementation of rate limiting based on ip
A text-classifier based on Naive Bayes.
Data-structure-implementation in java
Implemented Polynomial Regression from scratch and compared it against Scikit-Learn’s implementation using a real estate price dataset. The project demonstrates data preprocessing, exploratory analysis, model training, and evaluation with metrics like MSE and R². It provides insights into the impact of polynomial features on regression performance.
This is a trained model of a Reinforce agent playing CartPole-v1
Custom implementation of the C printf function using variadic arguments. Developed as part of the 42 school curriculum to master formatted output and low-level function design.
A .NET repository demonstrating the use of guard clauses for input validation and defensive programming. Includes implementations using both built-in .NET features and custom guard clauses, with practical examples for Order and Customer objects in a console application.
A PNG decoder module written in Monkey
This is a trained model of a Q Learning agent playing FrozenLake.
This project implements linear regression from first principles to predict YouTube ad revenue based on video metrics. Using NumPy, it covers hypothesis functions, cost optimization via gradient descent, and model evaluation. The custom model outperforms sklearn’s LinearRegression, offering insights into ad revenue trends.
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