💁‍♀️Your new best friend powered by an artificial neural network

Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

Official github repository for Fast Artificial Neural Network Library (FANN)

A python library for visualizing Artificial Neural Networks (ANN)

AForge.NET Framework is a C# framework designed for developers and researchers in the fields of Computer Vision and Artificial Intelligence - image processing, neural networks, genetic algorithms, machine learning, robotics, etc.

A lightweight C library for artificial neural networks

Books related to Artificial Intelligence, Machine Learning, Deep Learning and Neural Networks

Brain-inspired Cognitive Intelligence Engine (BrainCog) is a brain-inspired spiking neural network based platform for Brain-inspired Artificial Intelligence and simulating brains at multiple scales. The long term goal of BrainCog is to provide a comprehensive theory and system to decode the mechanisms and principles of human intelligence and its evolution, and develop artificial brains for brain-inspired conscious living AI in future Human-AI symbiotic Society.

🤖 A portable, header-only, artificial neural network library written in C99

Artificial Neural Network

Ruby library for interfacing with FANN (Fast Artificial Neural Network)

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

The simplest form of an artificial neural network explained and demonstrated.

Purely functional artificial neural network library implemented in Haskell.

Artificial intelligence/machine learning data structures and Swift algorithms for future iOS development. bayes theorem, neural networks, and more AI.

The LRP Toolbox provides simple and accessible stand-alone implementations of LRP for artificial neural networks supporting Matlab and Python. The Toolbox realizes LRP functionality for the Caffe Deep Learning Framework as an extension of Caffe source code published in 10/2015.

code for reproducing some of the diagrams in the paper "Multimodal Neurons in Artificial Neural Networks"

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

Building and training artificial neural networks (regression or classification) using the genetic algorithm.

🧠 🔎 🤖 Kitsune is an artificial neural network designed to detect and correlate Twitter profiles with similar behaviours.

Livermore Big Artificial Neural Network Toolkit

PHP wrapper for FANN (Fast Artificial Neural Network Library)

An artificial neural network written in Swift

This is the end-to-end Speech Recognition neural network, deployed in Keras. This was my final project for Artificial Intelligence Nanodegree @Udacity.

FANN (Fast Artificial Neural Network Library) bindings for Node.js

🧠 🦠 An artificial neural network and API to detect Windows malware, based on Ergo and LIEF.

GAN and VAE implementations to generate artificial EEG data to improve motor imagery classification. Data based on BCI Competition IV, datasets 2a. Final project for UCLA's EE C247: Neural Networks and Deep Learning course.

Code behind the work "Single Cortical Neurons as Deep Artificial Neural Networks", published in Neuron 2021

First Version.. Cryptocurrency Prediction with Artificial Intelligence (Deep Learning via LSTM Neural Networks)- Emirhan BULUT

Artificial Neural Networks / Python