A personal finance tracker app built with love, and Flutter

Tutorial para crear una aplicación web completa en PHP y MySQL donde combinamos sesiones, login, password hashes, CRUD, MVC

Simple free finance note for daily expenses

A travel expense manager made with Django, GraphQL, Next.js and Tailwind CSS

A simple tool for keeping track of personal finances. Free, quick and easy. Open source code. No extra features and subscriptions. Shows where your money goes. Various reports for the period.

Laravel based expense management system for managing site visit expenses of your employees.

An expense Tracker 🔥🔥 which lets you add transactions 🖊🖊 and generate a pdf report of all of your transactions📋📋

Managing money is a very complex and important task. Not being able to manage money properly can easily lead to waste. The main purpose of this application is to consider a user's money management, to keep track of how much money a user has earned in his daily life and how much money he has spent in any given day. The main purpose of this application is that a user can easily get rid of stress.

Spendify is an expense tracker app built using Flutter. It helps users manage their expenses by tracking their income and expenses, providing insights into spending patterns, and offering tools for budgeting and financial planning.

Automated expense tracker by parsing bank emails

The Project is about income expense management using Codeignitor, it is basically developed for a restaurant but can be customized for purposes.

A modern open source personal income tracking app.

Ibex Wallet - Personal Finance Manager - Web Application

A command-line financial tracking tool that enables users to manage their income and expenses locally, without any remote data storage

Xafe is a personal expense tracking app that helps users perfectly handle their personal finances such as income and expenditure in the best possible way, it will also help the users to list their expenses in different categories so that tracking them becomes easy with just a single click.

A small tool for recording your incomes and expenses. Designed to be simple, fast, and mobile-first. No integrations, no fancy analytics, just simply add your expenses and incomes and get a clear picture of where your money goes.

It's a android application. anyone create his expense in this application.

A Budget Tracking web application to help you control your daily expenses.

This is a backend service for managing and sharing daily expenses between users. The application allows users to add expenses and split them in three different ways: equally, by exact amounts, or by percentage. It also manages user details, validates input, and generates a downloadable balance sheet.

Easy to use lite expense tracker with focus on daily weekly monthly yearly spending limit.

Trackedify is a lightweight personal expense tracker designed to help you take control of your finances with ease.

A clean, modern web application to track and manage shared expenses between flatmates or roommates.

Expeny is an Expense management system using this you can manage your daily life expense and with advanced functionality like connect with your expense partner to manage Expenses together, data visualisation using graphs for better understanding.

Expense Manager Android App Utilizing OCR

A simple Flutter app to manage electricity bill.

Carteira virtual com calculo de ganhos e despesas

Open source personal income tracking mobile application

Expense Tracker is a web application that helps users manage and track their personal expenses. It allows for easy categorization, date management, and PDF report generation.

expense tracker whit react you can add transactions , delete , sort , income , expense , card balance

As the learning rate is one of the most important hyper-parameters to tune for training convolutional neural networks. In this paper, a powerful technique to select a range of learning rates for a neural network that named cyclical learning rate was implemented with two different skewness degrees. It is an approach to adjust where the value is cycled between a lower bound and upper bound. CLR policies are computationally simpler and can avoid the computational expense of fine tuning with fixed learning rate. It is clearly shown that changing the learning rate during the training phase provides by far better results than fixed values with similar or even smaller number of epochs.