chibivue is a minimal vuejs/core implementations (reactivity, virtual dom, component runtime, compiler, vapor, ssr, store, router, language tools). An online book for building your own Vue.js.

A deep learning framework created from scratch with Python and NumPy

nanoRLHF: from-scratch journey into how LLMs and RLHF really work.

📷 This repository is focused on having various feature implementation of OpenCV in Python. The aim is to have a minimal implementation of all OpenCV features together, under one roof.

Explanation on what a CNI (Container Network Interface) is and how to write it from scratch.

lm-scratch-pytorch - The code is designed to be beginner-friendly, with a focus on understanding the fundamentals of PyTorch and implementing LLMs from scratch,step by step.

Scripts of Machine Learning Algorithms from Scratch. Implementations of machine learning models and algorithms using nothing but NumPy with a focus on accessibility. Aims to cover everything from basic to advance.

Prithvi is an in-memory key-value database built from scratch in Java, without relying on external frameworks. It provides basic data storage operations, persistence to disk, and essential features like TTL expiry, automatic data management and LRU Cache

Repo for ML Models built from scratch such as Self-Attention, Linear +Logistic Regression, PCA, LDA. CNN, LSTM, Neural Networks using Numpy only

This repository guides you through the process of building a GPT-style Large Language Model (LLM) from scratch using PyTorch. The structure and approach are inspired by the book Build a Large Language Model (From Scratch) by Sebastian Raschka.

The sample code to study non-negative matrix and tensor factorization.

C++ library for building Scratch project players

Reinforcement Learning (RL)-based routing algorithm for SDN networks created from scratch using Python.

React implementation in Python 3, which runs on the client-side.

A C++ implementation of ChaCha20 & Poly1305 stream cipher described in RFC - 8439.

Scratch project player written in C++ and QML

Natural Language Processing Nanodegree from Udacity Platform, in which I implement Hidden Markov Model for POS Tagger, Bidirectional LSTM for English-French Machine Translation, and End-to-End LSTM-based Speech Recognition

A custom programming language built from scratch, includes full tokenizer, parser, interpeter in Python, variables, loops and more

Implementing most important basic building blocks of Deep Learning from scratch. My goal is to provide high quality Scratch Implementations of the fundamentals of Deep Learning and its applications, with interactive well documentated jupyter notebooks. All notebooks come along with implementations using Tensorflow, MXNet and Pytorch.

This contains a C++ code that implements a B-Tree data structure. A B-Tree is a self-balancing tree that can store and retrieve data efficiently. It is commonly used in databases and file systems.

A paper implementation and tutorial from scratch combining various great resources for implementing Transformers discussesd in Attention in All You Need Paper for the task of German to English Translation.

From Scratch Implementation of some popular Deep Learning Papers with PyTorch and Tensorflow

Scratch Interpreter for the CLI!

Implementation of build a LLM from scratch by Sebastian Raschka.

A better frontend for Scratch, built by the community, for the community

This is a C++ implementation of an AVL tree, which is a self-balancing binary search tree. An AVL tree maintains the balance factor of each node, which is the difference between the heights of its left and right subtrees. Whenever a node becomes unbalanced (its balance factor is either -2 or 2), the tree performs a rotation to restore the balance.

This repository contains reinforcement learning projects showcasing Q-Learning and Deep Q-Network (DQN) implementations. It features an autonomous taxi agent and a space invaders agent trained to complete their respective tasks.

Convolutional Neural Network implemenation from scratch in python numpy

This notebook consist of implementation of K-Mean clustering algorithm on an image to compress it from scratch using only numpy

This is my first Deep Learning project, which is a MNIST hand-written digits classifier. The model is implemented completely from scratch WITHOUT using any prebuilt optimization like Tensorflow or Pytorch. Tensorflow is imported only to load the MNIST data set. This model also uses 2 hidden layers with Adaptive Moment Optimization (Adam) and Drop-out regularization.