Visions of Chaos is a professional high end software application for Windows. It is simple enough for people who do not understand the mathematics behind it, but advanced enough for enthusiasts to tweak and customise to their needs. It is the most complete all in one application dealing with Chaos Theory and Machine Learning available. Every mode is written to give the best possible quality output. There are thousands of sample files included to give you an idea of what Visions of Chaos is capable of.
The TX Modular System is open source audio-visual software for modular synthesis and video generation, built with SuperCollider (https://supercollider.github.io) and openFrameworks (https://openFrameworks.cc).
It can be used to build interactive audio-visual systems such as: digital musical instruments, interactive generative compositions with real-time visuals, sound design tools, & live audio-visual processing tools.
This version has been tested on MacOS (0.10.11) and Windows (10). The audio engine should also work on Linux.
The visual engine, TXV, has only been built so far for MacOS and Windows - it is untested on Linux.
The current TXV MacOS build will only work with Mojave (10.14) or earlier (10.11, 10.12 & 10.13) - but NOT Catalina (10.15) or later.
You don't need to know how to program to use this system. But if you can program in SuperCollider, some modules allow you to edit the SuperCollider code inside - to generate or process audio, add modulation, create animations, or run SuperCollider Patterns.
Demonstration tutorial of retraining OpenAI’s GPT-2-small (a text-generating Transformer neural network) on a large public domain Project Gutenberg poetry corpus to generate high-quality English verse.
https://jalammar.github.io/illustrated-gpt2/
Other tutorial : https://medium.com/@ngwaifoong92/beginners-guide-to-retrain-gpt-2-117m-to-generate-custom-text-content-8bb5363d8b7f
https://github.com/minimaxir/gpt-2-simple
Example : http://textsynth.org/
Datasets :
https://www.kaggle.com/datasets
https://github.com/awesomedata/awesome-public-datasets
Scrap webpage with python :
https://www.crummy.com/software/BeautifulSoup/
https://github.com/EugenHotaj/beatles/blob/master/scraper.py
We call them "seeds". Each seed is a machine learning example you can start playing with. Explore, learn and grow them into whatever you like.
It's all a game of construction — some with a brush, some with a shovel, some choose a pen.
Jackson Pollock
…and some, including myself, choose neural networks. I’m an artist, and I've also been building commercial software for a long while. But art and software used to be two parallel tracks in my life; save for the occasional foray into generative art with Processing and computational photography, all my art was analog… until I discovered GANs (Generative Adversarial Networks).
Since the invention of GANs in 2014, the machine learning community has produced a number of deep, technical pieces about the technique (such as this one). This is not one of those pieces. Instead, I want to share in broad strokes some reasons why GANs are excellent artistic tools and the methods I have developed for creating my GAN-augmented art.
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/
https://github.com/eriklindernoren/PyTorch-GAN
https://heartbeat.fritz.ai/introduction-to-generative-adversarial-networks-gans-35ef44f21193
https://github.com/nightrome/really-awesome-gan
https://github.com/zhangqianhui/AdversarialNetsPapers
https://github.com/io99/Resources
https://github.com/yunjey/pytorch-tutorial
https://github.com/bharathgs/Awesome-pytorch-list
https://old.reddit.com/r/MachineLearning
http://www.codingwoman.com/generative-adversarial-networks-entertaining-intro/
https://medium.com/@jonathan_hui/gan-gan-series-2d279f906e7b
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ/videos
https://www.youtube.com/watch?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi&v=aircAruvnKk
https://www.youtube.com/watch?list=PLxt59R_fWVzT9bDxA76AHm3ig0Gg9S3So&v=ZzWaow1Rvho
Wildfire is a free and user-friendly image-processing software, mostly known for its sophisticated flame-fractal-generator. It is Java-based, open-source and runs on any major computer-plattform. There is also a special Android-version for mobile devices.
An open source collection of 20+ computational design tools for Clojure & Clojurescript by Karsten Schmidt.
In active development since 2012, and totalling almost 39,000 lines of code, the libraries address concepts related to many displines, from animation, generative design, data analysis / validation / visualization with SVG and WebGL, interactive installations, 2d / 3d geometry, digital fabrication, voxel modeling, rendering, linked data graphs & querying, encryption, OpenCL computing etc.
Many of the thi.ng projects (especially the larger ones) are written in a literate programming style and include extensive documentation, diagrams and tests, directly in the source code on GitHub. Each library can be used individually. All projects are licensed under the Apache Software License 2.0.
Artificial Neural Networks have spurred remarkable recent progress in image classification and speech recognition. But even though these are very useful tools based on well-known mathematical methods, we actually understand surprisingly little of why certain models work and others don’t. So let’s take a look at some simple techniques for peeking inside these networks.
We train an artificial neural network by showing it millions of training examples and gradually adjusting the network parameters until it gives the classifications we want. The network typically consists of 10-30 stacked layers of artificial neurons. Each image is fed into the input layer, which then talks to the next layer, until eventually the “output” layer is reached. The network’s “answer” comes from this final output layer.
One of the challenges of neural networks is understanding what exactly goes on at each layer. We know that after training, each layer progressively extracts higher and higher-level features of the image, until the final layer essentially makes a decision on what the image shows. For example, the first layer maybe looks for edges or corners. Intermediate layers interpret the basic features to look for overall shapes or components, like a door or a leaf. The final few layers assemble those into complete interpretations—these neurons activate in response to very complex things such as entire buildings or trees.
One way to visualize what goes on is to turn the network upside down and ask it to enhance an input image in such a way as to elicit a particular interpretation. Say you want to know what sort of image would result in “Banana.” Start with an image full of random noise, then gradually tweak the image towards what the neural net considers a banana (see related work in [1], [2], [3], [4]). By itself, that doesn’t work very well, but it does if we impose a prior constraint that the image should have similar statistics to natural images, such as neighboring pixels needing to be correlated.
Fragmentarium is an open source, cross-platform IDE for exploring pixel based graphics on the GPU. It is inspired by Adobe's Pixel Bender, but uses GLSL, and is created specifically with fractals and generative systems in mind.
Fractal 4D is a simple and very useful Adobe AIR app that enables you to draw beatiful fractal swirls.
Open source creative software catalog