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10-s-and-9-s-compliment-of-a-number

a simple python code to find the 10's and 9's compliment of a number, the 2's and 1's would be uploaded soon

2-dimensional-array

A 2 dimensional array using bubble sort

42-libft

My own implementation of some standard C Libraries

42-piscine-silicon-valley

a-star_traitbased

astar

DEPRICATED

bookstore

calculating-compliment-10-and-9-using-python

The last code I uploaded allowed for input of both the number(which you want to find compliment) and also the integral part.. but this code allows for only single input of the number

contribeautiful_data

autogenerated by [Contribeautiful](https://sethpainter.com/contribeautiful)

cracking-the-coding-interview

a few solutions to cracking the coding interview fourth edition. feel free to make changes

csdi

Codes for "CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation"

dit

Official PyTorch Implementation of "Scalable Diffusion Models with Transformers"

drjit

Dr.Jit — A Just-In-Time-Compiler for Differentiable Rendering

dstar-rust-crate

eagle

fb-messenger-bot

A starter Facebook Messenger Bot built in Python

forecasting

get-next-line

Created a function that works the same way as the C function getline().

instantsearch

⚡️ Libraries for building performant and instant search and recommend experiences with Algolia. Compatible with JavaScript, TypeScript, React and Vue.

lica

[NeurIPS 2020] PyTorch implementation of "Learning Implicit Credit Assignment for Cooperative Muti-Agent Reinforcement Learning"

models

Models and examples built with TensorFlow

multicast-magic

Persistent, asynchronous multicast messaging system built on top of TCP. Coordinator and participants are both implemented.

nacoss

nora-hub-app

oop-concept-with-c-

A bunch of code explaining OOP concept in c++..(nested classes using pointers)

pathfinding

Pathfinding library for rust

relationalmodeloperators

rustlings-solutions-5

Rustlings 5 (5.2.1) Solutions

scalation_py

unconditional-time-series-diffusion

Official PyTorch implementation of TSDiff models presented in the NeurIPS 2023 paper "Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting"