implementation-from-scratch
There are 1 repository under implementation-from-scratch topic.
punitpandey / ds-algo
Implementation of common Data Structures and Algorithms with Go
Language:Go 302- AlgoPlus
spirosmaggioros / AlgoPlus
AlgoPlus is a C++17 library with implemented data structures and algorithms for various topics(including machine learning)
algorithmsalgorithms-and-data-structurescomputer-sciencecppgraph-algorithmsmathematicstreetree-algorithmsalgorithms-interview-questionsimplementation-from-scratchimplementation-of-algorithmsimplementation-of-data-structuresinterview-preparationinterview-questionslinked-listmachine-learningmachine-learning-algorithmsmachinelearningimage-processingdata-structures-and-algorithmsLanguage:C++ 187 sanjay035 / Sketch2Color-anime-translation
Given a simple anime line-art sketch the model outputs a decent colored anime image using Conditional-Generative Adversarial Networks (C-GANs) concept.
Language:Jupyter Notebook 115patilharshal16 / data-structures
Computer science data structures and algorithms implementation from scratch
Language:Java 49amirrezarajabi / Neural-Network-implementation-from-scratch
implementation of neural network from scratch only using numpy (Conv, Fc, Maxpool, optimizers and activation functions)
Language:Python 7QuyAnh2005 / homemade-machine-learning
Understand and code some basic algorithms in machine learning from scratch
Language:Jupyter Notebook 7Sina-Taghizadeh / MATLAB_FEM_Implementation
Complete FEM implementation for 2D elasticity problems with truss, triangular and quadrilateral elements
Language:MATLAB 5Site1997 / RNN-implementation
An python implementation of RNN (without deep learning framework)
Language:Python 5Abhishekkjainn / Tripster
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
Language:Dart 4- Language:C 4
chaseungjoon / Baekjoon-Auto-Notion-V2
백준 문제를 풀고 코드 따로 저장 없이 Notion에 바로 커밋하기
Language:Python 3s-m-quadri / ask-iot
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.
Language:Python 3Utkarshthgr8 / employee-department-management
A Simple Employee Department Management System
Language:Java 3Babak-Gholamzadeh / implement-async-await
Implementing the async/await pattern from scratch
Language:JavaScript 2Vadimbuildercxx / Custom_nn
🧠 Collection of neural network implementations from scratch. Clean PyTorch implementations with educational comments and ready training loops.
Language:Python 2Adamantios / KPCA-LDA
KPCA and LDA implementations.
Language:Python 1BhekimpiloNdhlela / NASM-floating-point-addition
An implementation of the floating point addition and subraction using both NASM and C and comparing the two implementations.
Language:Assembly 1DavinciCode Card Game in C++. Algorithm from scratch
Language:C++ 1di-mi-ta / pylearn_ml191
pylearn_ml191: An implementation of some classical machine learning algorithm
Language:Jupyter Notebook 1Samahussien7 / Hamming-Network
Hamming Network implementation using pca implementation for reduction all from scratch
covariance-matrix-implementationeigenvalueseigenvalues-implementationeigenvectorseigenvectors-implementationhamming-networkimplementation-from-scratchinsertion-sortk-means-clusteringk-means-implementation-in-pythonmnist-datasetmnist-preprocessingpcapca-implementationpower-iterationunsupervised-learningvariance-analysisLanguage:Jupyter Notebook 1Uni-Creator / Handwritten_Number_Recognition_System
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.
Language:Python 1NanoGPT 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.
Language:Python 1whweasel / Python-Switch-Implementation
Implementation of Java, C, C#, and C++'s switch statement.
Language:Python 1AdriandLiu / Implementation-of-MLP-with-Numpy
Implementation of Multi-Layer Perceptron with Numpy and CNN with PyTorch in Image Classification
Language:Jupyter Notebook 0Akashtamate / Snacks-masala-mixer-
Design and development of a crispy Corn snack masala mixing machine
Language:C++ 0aryyawijaya / fuzzy-logic
Build fuzzy logic to find the best 10 workshops from 100 workshop data.
Language:Jupyter Notebook 0Build a genetic algorithm to find the values of x and y that produce the minimum value of the function h(x, y).
Language:Jupyter Notebook 0Build a kNN (k-Nearest Neighbor) model from scratch to predict 10 test data.
Language:Jupyter Notebook 0jaroslawroszyk / StringClass
Writing the string class from scratch as an exercise
Language:C++ 0nekcht / ml-classic-scratch
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.
Language:Jupyter Notebook 0smebad / Transformer-from-Scratch
This project is a complete, from-scratch implementation of the Transformer architecture as described in the "Attention is All You Need" paper.
Language:Python 0TAJAMUL11 / MeeladPort
Meelad_Badri Portfolio. Developed it according to the design provided by the designer. The Portfolio has eye catching interface and latest technologies.
Language:CSS 0triangle-motelti / mini-bash
a simple Unix shell supporting commands, redirections, pipes, built-ins, env vars, and signal handling in C.
Language:C 0paulinamoskwa / from-scratch
Collection of implementations from scratch (mostly ML)
Language:Jupyter NotebookThis 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.
Language:Jupyter NotebookSmeltier / git-from-scratch
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.
Language:Python
