akkireddy95

A-dataset-for-a-more-challenging-Non-MNIST-like-classification-task

The MNIST dataset is one of the best known image classification problems out there, and a veritable classic of the field of machine learning. This dataset is more challenging version of the same root problem: classifying letters from images. This is a multiclass classification dataset of glyphs of English letters A - J. This dataset is used extensively in the Udacity Deep Learning course, and is available in the Tensorflow Github repo (under Examples). I'm not aware of any license governing the use of this data, so I'm posting it here so that the community can use it with Kaggle kernels. Content notMNIST _large.zip is a large but dirty version of the dataset with 529,119 images, and notMNIST_small.zip is a small hand-cleaned version of the dataset, with 18726 images. The dataset was assembled by Yaroslav Bulatov, and can be obtained on his blog. According to this blog entry there is about a 6.5% label error rate on the large uncleaned dataset, and a 0.5% label error rate on the small hand-cleaned dataset. The two files each containing 28x28 grayscale images of letters A - J, organized into directories by letter. notMNIST_large.zip contains 529,119 images and notMNIST_small.zip contains 18726 images. Acknowledgements Thanks to Yaroslav Bulatov for putting together the dataset.

akkireddy

akkireddy95

Config files for my GitHub profile.

akkireddy95.github.io

A lightweight, customizable single-page personal portfolio website template built with JavaScript and Sass

All-about-AI-Python-Edition

DevOps-guide

DevOps Tutorial for Beginners will help you learn DevOps concepts and DevOps tools with examples and demos.

Linkedin-Learning-Courses-Downloader

Linkedin Learning Courses Downloader is a simple python scraper tool that downloads video lessons from Linkedin Learning

Online-Banking-system

The project’s primary goals consist of: A robust and effective web based online banking system. Extending functionality without compromising the security. Personal banking services that gives you complete control over all your banking demands online. Simple and easy user interface to work with. Online Banking features: Registration for online banking by Admin. Adding Beneficiary account by customer. Transferring amount to the beneficiary added by customer. Staff must approve for beneficiary activation before it can be used for transferring funds. Customer gets to know his last login date and time each time he logs in. Customer can check last 10 transactions made with their account. Customer can check their account statement within a date range. Customer can request for ATM and Cheque Book. Staff will approve requests for ATM card and cheque book. Admin can add/edit/delete customer as well as staff. All three of them(customer, staff & admin) can change their password. Staff and Admin Login pages are hidden from customer for security purpose. Security: Each and every input is passed through mysql_real_escape_string() to remove special characters from the string so that user can’t submit arbitrary input. It protects from attacks like Sql Injection and Cross Site Scripting(XSS). Passwords are encrypted with SHA- 1 hashing algorithm and then stored in database. Passwords are stored as encrypted hashes with an additional random salt for added security. Note: If we talk about security,above mentioned points would provide no security at all but it will work for beginners. Being into web app pentesting,I very well know this project will not be considered secure. It needs atleast a more effective filtering mechanism, better hashing algorithm, a better salting procedure for storing passwords and some other things too. Database: The database contains customer, staff & admin tables. Each customer has its own virtual passbook linked with its account number. Staff, customer and, admin have their details and password in their respective tables with all the details. A separate table for beneficiary is included in the database. A table for cheque book and ATM requests is included. This project is divided into three hierarchical parts: The index page for the customer login. A hidden staff login page. A hidden admin login page. Usage: 1.Install XAMPP or something similar. 2.Copy all the files to c:/xampp/htdocs/banking/ 3.Create a db named as 'bnak_db' and import the bank_db.sql from phpMyAdmin. 4.change the password in _inc/dbconn.php file accordingly. 5.visit localhost/banking (customer index page) 6.visit localhost/banking/adminlogin.php (admin login) 7.visit the localhost/banking/staff_login.php (staff login)

Voting-API

Voting

Voting-rate-predection-with-open-API

I would like you to create a piece of software that compiles voting participation from 1973 until the latest parliamentary elections. For each year we want to know which election district has the highest number of voters and how much the percentage was (in percentages). The result should contain about the following:

akkireddy95/Voting-rate-predection-with-open-API

I would like you to create a piece of software that compiles voting participation from 1973 until the latest parliamentary elections. For each year we want to know which election district has the highest number of voters and how much the percentage was (in percentages). The result should contain about the following:

akkireddy95/Machine-Learning---AI-Foundations--Recommendations

This project-based is how to use machine learning to build programs that can make recommendations. In this project i walks you through a hands-on lab building a recommendation system that is able to suggest similar products to customers based on past products they have reviewed or purchased. The system can also identify which products are similar to each other.

akkireddy95/ethereum

akkireddy95/ESNapp

App to support ESN maribor projects