Generative AI - From Big Picture, to Idea, to Implementation

What you'll learn
How to implement Generative AI models. We focus on proper concept implementation and relevant code (no administrative code)
Get to know the broad spectrum of GAI applications and possibilities tangibly eg. 3D object generation, interactive image generation, and text generation
How to identify great ideas in the GAI space and make best use of already developed models for realising your projects and ideas
How to augment your dataset such that it ultimately improves your machine learning performance eg. for classifiers of rare diseases
Learn about the ethical side: what are the concerns around GAI, incl. deep fakes, etc.
The technical side: from the evolution of generative models, to the generator-discriminator interplay, to common implemenation issues and their remedies
Requirements
No hard prerequisites
Nice-to-have: coding skills and pre-knowledge in machine learning
Description
Recently, we have seen a shift in AI that wasn't very obvious. Generative Artificial Intelligence (GAI) - the part of AI that can generate all kinds of data - started to yield acceptable results, getting better and better. As GAI models get better, questions arise e.g. what will be possible with GAI models? Or, how to utilize data generation for your own projects?
In this course, we answer these and more questions as best as possible.
There are 3 angles that we take
Tech angle: we see what GAI models exist and how they are implemented. We will focus on only relevant parts of the code and not on administrative code that won't be accurate a year from now (it's one google away). Further, there will be an excursion: from computation graphs, to neural networks, to deep neural networks, to convolutional neural networks (the basis for image and video generation).
The architecture list is down below.
Application angle: we get to know many GAI application fields, where we then ideate what further projects could emerge from that. Ultimately, we point to good starting points and how to get GAI models implemented effectively.
The application list is down below
Ethical angle/ Ethical AI: we discuss the concerns of GAI models and what companies and governments do to prevent further harm.
Enjoy your GAI journey!
List of discussed application fields
Cybersecurity 2.0 (Adversarial Attack vs. Defense)
3D Object Generation
Text-to-Image Translation
Video-to-Video Translation
Superresolution
Interactive Image Generation
Face Generation
Generative Art
Data Compression with GANs
Domain-Transfer (i.e. Style-Transfer, Sketch-to-Image, Segmentation-to-Image)
Crypto, Blockchain, NFTs
Idea Generator
Automatic Video Generation and Video Prediction
Text Generation, NLP Models (incl. Coding Suggestions like Co-Pilot)
GAI Outlook
etc.
Generative AI Architectures/ Models that we cover in the course (at least conceptually)
(Vanilla) GAN
AutoEncoder
Variational AutoEncoder
Style-GAN
conditional GAN
3D-GAN
GauGAN
DC-GAN
CycleGAN
GPT-3
Progressive GAN
BiGAN
GameGAN
BigGAN
Pix2Vox
WGAN
StackGAN
etc.
Who this course is for
Potential entrepreneurs, as we will provoke various project ideas
Tech-enthusiasts that want to learn/ stay up-to-date with the newest advancements in AI
Visionaries that want to help shaping the future with (G)AI
Everyone who would enjoy a smooth journey through the world of Generative AI
Homepage

https://www.udemy.com/course/generative-ai/