Artificial neural networks (ANN) are computational systems that "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.
Simple convolutional neural network (purely numpy) to classify the original MNIST dataset. My first project with a convnet. 🖼
Some basic examples to get started with ML using python
This repository contains a comprehensive Python script for building and training an Artificial Neural Network (ANN) model to predict customer churn using customer data. The code includes extensive data preprocessing steps, such as scaling, one-hot encoding, and visualization, to ensure the model's accuracy and effectiveness.
Built and tested 6 supervised machine learning algorithm to develop a predictive classification model to classify 13000+ projects as success or failure.
This could include projects related to meditation and mental health, such as guided meditation apps or tools for tracking mood and stress levels. The goal of "OpenConscience" would be to promote technology that helps people live more mindfully and ethically in their personal and professional lives.
An interface to build general artificial neural networks using TensorFlow.
Introductory level artificial neural network
Application to predict heart disease using logistic regression
A fast artificial intelligence library to automatically manage your websites.
Critters with small brains trained with a genetic algorithm, now in JavaScript!
Introducing Artificial Intelligence to young people by developing a gaming environment where creativity and knowledge take place.
A Unimodal Neural Network-Based Person Authentication System Based on the Sound of the Signing
Machine Learning lab codes
Certificates of the Deep Learning Specialization from Coursera (2020)
Repository for the Deep Learning Specialization by DeepLearning.AI on Coursera.
Deep Learning with Convolutional Neural Networks
This project predicts the probability of graduate admission based on various inputs such as research papers, cgpa etc.
This code uses computational graph and neural network to solve the five-layer traffic demand estimation in Sioux Falls network. It also includes comparison of models and 10 cross-validations.