Darkwolf007's Stars
camenduru/stable-diffusion-webui-colab
stable diffusion webui colab
jakob-beetz/ifcopenshell-notebooks
Interactive Jupyter Notebooks to teach working with IFC files using ifcopenshell
mserraureta/MPDA-APY
Advanced Python Course Assignments
epfml/ML_course
EPFL Machine Learning Course, Fall 2023
renatogcruz/Data-science-for-architecture
Repository for data science study for architecture, engineering and construction (AEC)
hmcarchitects/hmcarchitects.github.io
This repository will contain all the grasshopper scripts developed by the Digital Practice team within HMC Architects.
Sonderwoods/GrasshopperScribbles
A few cool grasshopper scripts such as template and eventhandlers
GeometryCollective/boundary-first-flattening
ZhiPeng-Han/GrasshopperFoundation
Saiga1105/Scan-to-BIM-Grasshopper
Scan-to-BIM toolbox for Grasshopper Rhino including mesh/pcd segmentation, classification and reconstruction
aussieBIMguru/Revit-Files
Files to accompany Aussie BIM Guru videos.
gwyllo/acadia
ACADIA machine learning workshop code and resources
samgr55/DigitalFUTURES_Workshops
DigitalFUTURES Workshops
mohammedbehjoo/-artificial-intelligence-in-architecture-exploring-GANs
This workshop is going to be seven sessions of 3 hours. It consists of three major parts: first, Fundamentals of deep learning, which is about the history of artificial intelligence and the relationship between AI, machine learning, and deep learning. It is also about the mathematics of neural networks. After that, we are going to learn about the basics of neural networks and machine learning. Second, we are going more in-depth in computer vision; we train a neural network from scratch. Then we learn about “transfer learning,” which means how to use a pre-trained neural network. Finally, and most importantly, the last part is about “Generative adversarial networks” (GAN). We will have some experiments with “ neural style transfer” as an example. After that, we will learn about GANs, specifically CycleGAN, and how to implement them. After this workshop, you know the basics of artificial intelligence, the relationship between machine learning and deep learning. You understand how neural networks work; you can make and implement deep learning models to classify images. You can also make Generative Adversarial Networks to synthesize new images and broaden your horizons.
tsuday/ObjCanvas.js
Load *.obj file or depth map array and draw them as 3D object on Canvas. Drawn canvas image can be downloaded as file.
HassanEmam/ifcjs-6d
https://hassanemam.github.io/ifcjs-6d/
terkelg/awesome-creative-coding
Creative Coding: Generative Art, Data visualization, Interaction Design, Resources.
NajaJohansen/LCA-Special-course_2022
ThatOpen/ifcjs-crash-course
Exercises of the IFC.js crash course.
BarusXXX/machine_learning_examples
A collection of machine learning examples and tutorials.
torrinworx/Cozy-Auto-Texture
A Blender add-on for generating free textures using the Stable Diffusion AI text to image model.
MahmoudAbdelRahman/GH_CPython
CPython plugin for Rhino-Grasshopper
IaaC/Programming_with_python_2122
This repository contains the course materials for the precourse on computer programming with python for the Master in City and Technology and the Master in Robotics and Advanced Construction.
seanjyu/truss_opt_grasshopper
zachpino/generative-design-workshop-s21
Welcome to the course materials for the IIT Institute of Design *Generative Design Workshop* introductory course to computational design approaches, algorithmic design tools, and experimental data visualization and physicalization.
RizwanMunawar/Extraction-of-frames-from-single-video-computer-vision-
Extraction of frames from single video using OpenCV
tmshv/swarm
Multi Agent based simulation framework
StDrunks/Living-Loop
Sanakan8472/copy-dialog-lunar-lander
Play lunar lander in you windows file copy dialog
jeeliz/jeelizWeboji
JavaScript/WebGL real-time face tracking and expression detection library. Build your own emoticons animated in real time in the browser! SVG and THREE.js integration demos are provided.