HOU2TOUCH
AI/ML in Visuals
EARLY BIRD DISCOUNT ENDS:
WHO IS TEACHING?
Vadim Epstein
MEDIA ARTIST, DIRECTOR, EDUCATOR, CODER, VJ
Former IT consultant and casual theoretical physicist, combining serious technical background, strong corporate experience and vivid creative mind. Has worked in various fields such as net.art and science art since 1996, eventually focused on visual media with stochastic algo narratives.
As an artist and curator, had made visuals for hundreds of concerts, festivals, parties, and commercial events.
The artworks have been exhibited worldwide in Montreal, Vancouver, Stuttgart, Paris, London, Moscow, Lille; highlighted on the conferences NeurIPS 2020 / 2021 / 2022, CVPR 2021; sold as NFT collections, etc.
Besides commercial and personal projects, has delivered numerous talks, workshops and training courses.
since 2002
Leading russian VJ
since 2017
Focused on AI/ML creative methods
AI/ML in Visuals Course Program
SKILL LEVEL
Beginner
Intermediate
START
23.10.2023
DURATION
7 weeks
FREQUENCY
2 lessons per week
COMMITMENT
10 hours weekly
SOFTWARE
Pytorch
TouchDesigner 099 / 2022
Outcomes from the course
- Comprehension of the Neural Networks principles.
- "Low-level" experiments with visual ML.
- Comprehension of the modern NN architectures (convolutional, transformers, diffusion).
- Applied skills to train & use modern GAN models.
- Practical comprehension of the modern generative paradigm with multimodal models.
- Skills to use and customize Diffusion methods.
- Use of generative AI/ML in the integrated projects.
1.1. Intro to the course
Python, Colab vs local, libraries and modules. Framework setup.
1.2. Introduction to the neural networks
Neuron, graph, loss function. Training as an optimization process. CPPN (image reproduction)
2.1. CNN: concept, features
Convolutions as graphics filters, how they process & store data.
2.2. Architectures & building principles
Style transfer, autoencoders, GAN. Features, iterative pipeline, network extensions.
3.1. Modern GANs
ProGAN, StyleGANs. Latent space - animation, blending, projection, special tricks.
3.2. StyleGAN2 training
Data preparation, training process. Augmentation.
4.1. Image transformations (image-to-image)
From pix2pix to StarGAN2. Applied methods overview.
4.2. Integration with TD
OSC, NDI, Spout, built-in Python
5.1. Multimodal synthesis with foundation models
CLIP, early iterative methods. CPPNs, Aphantasia, VQGAN.
5.2. Transformers & Diffusion models
Principles & specifics. Denoised & latent diffusion.
6.1. Stable Diffusion as the current generative tool #1
Architecture, use cases: txt2img, img2img, interpolations, ..
6.2. SD control & finetuning
Controlnet. Textual inversion, custom diffusion, LoRA, ..
Development of the own project with visual AI/ML methods
Early bird discount ends:
AI/ML in Visuals Course
666€ 555€
- Access to the course materials & sessions
- Access to the shared chat
- Homeworks and course topics' discussions
- Final exam
Incl. VAT: 88.61 €
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contact us to avoid double tax
WHY IS COURSE UNIQUE?
Let's start!