Lambda called on lambda finds/creates lambda, each with lazy-evaled 256 bit global DAG ids. Each lambda has 2 child lambdas. A kind of number that is a universal-pattern-calculus-combinator. Axgob.js (in dagball project, lib dir) is incomplete newer version of this. A fork-editable multiverse of all possible lambdas.

Prediction of Students' Academic Performance Dataset: Cart Trees, Random Forest, Cross Validation and Neuralnet

[DEPRECATED IN FAVOR OF /runyanjake/evolutions] A summer project aimed to build a multi-function simulation centered around an evolutionary neural net module (using Google TensorFlow or proprietary implementation). Concepts stem from https://www.youtube.com/watch?v=ghwXmA1s1I4 .

A game where you slide a 2d surface (the screen) through a space of possible 300-dimensional voxels (up to 1000) defined by small pieces of code and artistic use of the high dimensional editing tools. Balls roll around pushing on the 300 dimensional heightmap, moving the screen in 300d.

Simple chatbot implementation.

Fit four different neural networks: (a) Two distinct single hidden layer neural networks. (b) Two distinct neural networks with two hidden layers. Compare the accuracy of these four Neural networks among them. Also compare it to other classification methods.

CAP6619 Deep Learning FAU CS master's Fall 2018

Tutorials, courses, solved exercises and examples of Deep Learning

Building a neural network predicting football match score

Implementation of Neural Processes with chainer

Functioning Conversational AI using custom models.

Compute your R 'neuralnet' neural network using cpp code

R script to classify trojan vs. benign traffic utilizing various machine learning models

final versions of projects from TJHSST AI class 2018-19

Implementation of a Feed Forward Neural Networks.

Notas e experimentos em diversos métodos e algoritmos de Aprendizado de Máquina visando a criação de apontamentos acessíveis e didáticos.

The trial about construction ML model by R and memo. Rによる機械学習モデルの構築についての試行とメモ.

In this project I have implemented the forward function of a Neural Network composed of sparsely connected layers. In order to parallelize the forward function I have built two implementation: one uses OpenMP and the other uses CUDA.

This repository contains files containing attempts at solutions to problems for lab sessions part of a lecture series prepared for a 2-week introductory course to machine learning by Etienne A.D Pienaar