There are 1 repository under implementation-from-scratch topic.
Implementation of common Data Structures and Algorithms with Go
AlgoPlus is a C++17 library with implemented data structures and algorithms for various topics(including machine learning)
Given a simple anime line-art sketch the model outputs a decent colored anime image using Conditional-Generative Adversarial Networks (C-GANs) concept.
Computer science data structures and algorithms implementation from scratch
implementation of neural network from scratch only using numpy (Conv, Fc, Maxpool, optimizers and activation functions)
Understand and code some basic algorithms in machine learning from scratch
Complete FEM implementation for 2D elasticity problems with truss, triangular and quadrilateral elements
An python implementation of RNN (without deep learning framework)
Tripster is a real-time domestic flight booking application built using Flutter and powered by the Tripjack Air API. This cross-platform app provides a seamless booking experience, allowing users to search for flights, compare prices, and make reservations from 137 domestic airports across India
백준 문제를 풀고 코드 따로 저장 없이 Notion에 바로 커밋하기
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.
A Simple Employee Department Management System
Implementing the async/await pattern from scratch
🧠 Collection of neural network implementations from scratch. Clean PyTorch implementations with educational comments and ready training loops.
KPCA and LDA implementations.
An implementation of the floating point addition and subraction using both NASM and C and comparing the two implementations.
DavinciCode Card Game in C++. Algorithm from scratch
pylearn_ml191: An implementation of some classical machine learning algorithm
Hamming Network implementation using pca implementation for reduction all from scratch
A Handwritten Number Recognition System built from scratch using Deep Learning from Scratch. The model is trained on digit images and can classify handwritten numbers with high accuracy.
NanoGPT is a lightweight GPT-style language model designed for text generation. It supports training on custom datasets, pretrained model inference, and minimal dependencies for efficient experimentation.
Implementation of Java, C, C#, and C++'s switch statement.
Implementation of Multi-Layer Perceptron with Numpy and CNN with PyTorch in Image Classification
Design and development of a crispy Corn snack masala mixing machine
Build fuzzy logic to find the best 10 workshops from 100 workshop data.
Build a genetic algorithm to find the values of x and y that produce the minimum value of the function h(x, y).
Build a kNN (k-Nearest Neighbor) model from scratch to predict 10 test data.
Writing the string class from scratch as an exercise
Ordinary Least Squares, Ridge Regression, Expectation Maximization, Full Bayesian Inference, Bayes Classifiers, kNN, and MLP core algorithms from scratch. Some auxiliary functions are also used.
This project is a complete, from-scratch implementation of the Transformer architecture as described in the "Attention is All You Need" paper.
Meelad_Badri Portfolio. Developed it according to the design provided by the designer. The Portfolio has eye catching interface and latest technologies.
a simple Unix shell supporting commands, redirections, pipes, built-ins, env vars, and signal handling in C.
Collection of implementations from scratch (mostly ML)
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.
Disclaimer: The content of the repository is based on the tutorial made by Thibault Polge. My goal is just to learn more about Git. Every implementation was done NOT just by copying and pasting, but by trying to understand each line and each command that was being typed.