Generative AI - From Big Picture, to Idea, to Implementation
- Descripción
- Currículum
- Reseñas
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:
-
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.
-
Tech angle: we see what GAI models exist. 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.
-
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.
-
1Let's get startedVideo lesson
-
2What is Generative AI?Video lesson
-
3Your Instructor MartinVideo lesson
It would be great if you could add me on LinkedIn (Search: "Martin Musiol" in Munich, Germany, and then, you should see my profile picture. ). I am happy to exchange ideas with you or meet up.
-
4The Course OverviewVideo lesson
-
5Your Feedback is valuableVideo lesson
-
8Broad Application Fields and PotentialVideo lesson
-
9GAI in Top Strategic Tech TrendsVideo lesson
-
10Example: Face GenerationVideo lesson
-
11Example: Do-as-I-do-motion TransferVideo lesson
-
12[Part 1] Example: GAI-generated Art AND the Interlock with Crypto & NFTsVideo lesson
-
13[Part 2] Example: GAI-generated Art AND the Interlock with Crypto & NFTsVideo lesson
-
14[Part 3] Example: GAI-generated Art AND the Interlock with Crypto & NFTsVideo lesson
-
15On why does Generative AI matter?Cuestionario
-
283D-Object GenerationVideo lesson
-
293D-Object Generation - IdeasVideo lesson
-
30on 3D-Object GenerationCuestionario
-
31Interactive Image GenerationVideo lesson
-
32[Part 1] Conditional GAN (cGAN) DemoVideo lesson
-
33[Part 2] Conditional GAN (cGAN) DemoVideo lesson
-
34How does a GauGAN work - IdeasVideo lesson
-
35How to augment Data and WhyVideo lesson
-
36What Data Augmentation Techniques exist & what is their EffectivenessVideo lesson
-
37Data Augmentation with a GAN - DemoVideo lesson
-
38Data Augmentation: Lessons Learnt & OutlookVideo lesson