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

3DGS_PSL_UPENN

AeroPython

Classical Aerodynamics of potential flow using Python and Jupyter Notebooks

SEU_2019

Urban and Architectural Modeling with Machine Learning and Big Data

urban-modeling-at-scale

auto_viz

Test

big_data_benchmarks

big data technologies comparisons for cleaning, manipulating and generally wrangling data in purpose of analysis and machine learning.

click_that_hood

A game where users must identify a city's neighborhoods as fast as possible

colouring-london

Collecting data on London's buildings

Deep-Learning-in-Asset-Pricing

https://arxiv.org/abs/1805.01104

deeplearning-models

A collection of various deep learning architectures, models, and tips

EngComp

A sophomore course in engineering computation

fenics-tutorial

Source files and published documents for the FEniCS tutorial.

hamiltonian-nn

Code for our paper "Hamiltonian Neural Networks"

kep

mslearn-dp100

Lab files for Azure Machine Learning exercises

nbdev_test

nerf

Code release for NeRF (Neural Radiance Fields)

playable_city

Webplatform for crowd sourcing information about vacant lots in London

reference_project

spark-movie-lens

An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset

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.

swipelabel

Simple phone web app to label images with swipe gestures.