sevamoo

SOMPY

A Python Library for Self Organizing Map (SOM)

data_driven_modeling_2018

Codes and presentations for Data Driven Modeling course at ETH Zurich, chair for Computer Aided Architectural Design (CAAD) 2018

data_driven_modeling_2016

Codes and presentations for Data Driven Modeling course at ETH Zurich, chair for Computer Aided Architectural Design (CAAD)

Force_Density_Method

An implementation of Force Density Method (FDM) in Python

data_driven_modeling_2017

Codes and presentations for Data Driven Modeling course at ETH Zurich, chair for Computer Aided Architectural Design (CAAD) 2017

cityastext

An interactive map showing the center of cities, towns and villages

Structural_Form_Machine_Learning

3d

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

nerf

Code release for NeRF (Neural Radiance Fields)

urban-modeling-at-scale

SEU_2019

Urban and Architectural Modeling with Machine Learning and Big Data