This repository contains the code to reproduce the results in the paper A Variational algorithm for Quantum Neural Networks, accepted in the International Conference on Computational Science 2020, Quantum Computing track.

This repository contains the code to reproduce the results in the paper "A Variational Algorithm for Quantum Single Layer Perceptron" submitted to The 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science

➿ Includes Single Neuron Implementation and Single Layer(Multi Neuron) Implementation

This repository contains a collection of fundamental topics and techniques in machine learning. It aims to provide a comprehensive understanding of various aspects of machine learning through simplified notebooks. Each topic is covered in a separate notebook, allowing for easy exploration and learning.

chronic kidney disease detection using different neural network technique

Python implementation of the simple perceptron or also known as a single-layer neural network, is a binary classification algorithm by Frank Rosenblatt based on the neural model of Warren McCulloch and Walter Pitts developed in 1943.

My simple and basic implementation of a perceptron using python

Language recognition using Perceptrons

Artificial Neural Network Algorithms

Neural network models

Contains work done mainly on Neural Networks and Machine Learning as a part of my interest for Data Science. The codes are in python and uses TensorFlow for Deep learning

Binary classification using kNN, neighborhood and perceptron from scratch in python. Using the stressed/Not Stressed dataset

This project contains many topics inside. Please check the video link below for details

Important Ml Codes and Concepts(Sorted)

Implementing, describing and testing a single layer perceptron for predicting diabetes

Single Layer Perceptrons (SLPs) and Multi-Layer Perceptrons (MLPs) from scratch, only with numpy, for classification and regression. MLPs with Keras for time-series prediction.

This repository contains the python implementations of a few soft computing algorithms.