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.

Implementation of Neural Processes with chainer

Tutorials, courses, solved exercises and examples of Deep Learning

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.

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.

Implementation of a Feed Forward Neural Networks.

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

CAP6619 Deep Learning FAU CS master's Fall 2018

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

Simple chatbot implementation.

Building a neural network predicting football match score

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

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.

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.

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

This repository contains Jupyter Notebook file containing the code to compare different sklearn classifiers on a dataset. Then it saves the output .png results in the working folder.

Deep Learning Programming Assignment (MSE, Classifiers(Softmax, SVM), Backpropagation, Neural Network, MNIST CNN, ResNet)