A flexible artificial neural network builder to analyse performance, and optimise the best model.

📄 The math behind the neural network used for Olivia

Hierarchical self-organizing maps for unsupervised pattern recognition

Aika is a new type of artificial neural network designed to more closely mimic the behavior of a biological brain and to bridge the gap to classical AI. A key design decision in the Aika network is to conceptually separate the activations from their neurons, meaning that there are two separate graphs. One graph consisting of neurons and synapses representing the knowledge the network has already acquired and another graph consisting of activations and links describing the information the network was able to infer about a concrete input data set. There is a one-to-many relation between the neurons and the activations. For example, there might be a neuron representing a word or a specific meaning of a word, but there might be several activations of this neuron, each representing an occurrence of this word within the input data set. A consequence of this decision is that we have to give up on the idea of a fixed layered topology for the network, since the sequence in which the activations are fired depends on the input data set. Within the activation network, each activation is grounded within the input data set, even if there are several activations in between. This means links between activations serve two purposes. On the one hand, they are used to sum up the synapse weights and, on the other hand they propagate the identity to higher level activations.

🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.

Image Classification for Android using Artificial Neural Network using NumPy and Kivy.

A topology and parameter evolving universal learning network.

A Multi Layer Perceptron (MLP) Artificial Neural Network (ANN) Framework Developed in C for Machine Learning (ML) and Deep Learning (DL)

Very Simple and Basic Implementation of Compositional Pattern Producing Network in TensorFlow

LoveBrains is a simulator of artificial intelligence based on a plugin system.

🚥 An Artificial Neural Network based Traffic Light Controller for intersections. Computational Intelligence class final project.

ADVANCED COLLECTION OF MACHINE LEARNING ALGORITHMS SMARTLY PUT TOGETHER

Experimental Artificial Neural Network, with non-derivative learning-algorithm

Self Diving Car Game using Pytorch and Kivy framework in Python

The project trains an Artificial Neural Network which can predict whether a visitor will generate revenue for the company or not.

A self-organizing feature map which is trained on a random three-dimensional (RGB) input space.

Artificial Intelligence

Simple convolutional neural network (purely numpy) to classify the original MNIST dataset. My first project with a convnet. 🖼

Spiking Artificial Neural Network With Reward Driven Hebbian Learning

This is an intelligent seat allocation system made using Flask

This repository contains a collection of machine learning and deep learning algorithms I implemented from scratch using Python and C++.

Artificial Neural Network from basic to advanced using PyTorch

This is an artificial neural network that uses Python to predict whether a customer will leave a bank based on multiple independent variables (some of which are credit score, age and gender).

Creating a feed forward neural network from scratch and testing it

According to WHO(world health organization) survey in 2014, the dataset contains nourished and malnourished child information (under 5). The job is to find out whether a child is malnourished or not when a new data will come applying machine learning algorithm.

Resources to kick start your machine learning project

Implementation and visualization of a Neural Network in JavaScript