Implementation of common Data Structures and Algorithms with Go
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Updated
Oct 16, 2023 - Go
Implementation of common Data Structures and Algorithms with Go
Given a simple anime line-art sketch the model outputs a decent colored anime image using Conditional-Generative Adversarial Networks (C-GANs) concept.
AlgoPlus is a C++17 library for complex data structures and algorithms
An python implementation of RNN (without deep learning framework)
Computer science data structures and algorithms implementation from scratch
Understand and code some basic algorithms in machine learning from scratch
Python implementation of the neural networks without using any libraries from scratch, for prediction using the pre-trained weights
KPCA and LDA implementations.
implementation of neural network from scratch only using numpy (Conv, Fc, Maxpool, optimizers and activation functions)
Implementing the promise pattern in JavaScript from scratch (step by step)
An implementation of the floating point addition and subraction using both NASM and C and comparing the two implementations.
A Python-based command-line tool developed as part of a research project on Machine Learning and IoT. It utilizes a custom implementation of the TF-IDF algorithm to provide interactive and concise three-point answers to IoT-related queries.
Implementing the async/await pattern from scratch
implementing statistical stuff from scratch
Implementation of Java, C, C#, and C++'s switch statement.
A Simple Employee Department Management System
Implementation of C standard library memory-related functions: malloc(), calloc(), realloc(), free() from scratch.
Hamming Network implementation using pca implementation for reduction all from scratch
Meelad_Badri Portfolio. Developed it according to the design provided by the designer. The Portfolio has eye catching interface and latest technologies.
This project aims to build a complete pattern recognition system to solve classification problems using the k-Nearest Neighbors (KNN) algorithm. To classify chest X-ray images into three categories: COVID-19 positive, pneumonia positive, and normal. To achieve this, we utilize the COVID-19 Chest X-ray dataset available on Kaggle.
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