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
- No hard prerequisites
- Nice-to-have: coding skills and pre-knowledge in machine learning
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
Interactive Image Generation
Data Compression with GANs
Domain-Transfer (i.e. Style-Transfer, Sketch-to-Image, Segmentation-to-Image)
Crypto, Blockchain, NFTs
Automatic Video Generation and Video Prediction
Text Generation, NLP Models (incl. Coding Suggestions like Co-Pilot)
Generative AI Architectures/ Models that we cover in the course (at least conceptually):
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
I have been coding since 2006 - wow! What year is it?
I wasn't clear about my career path back in the day. It was through a course called 'self-learning systems' in 2012 that I realized what I am passionate about - artificial intelligence!
In various projects, I focused on methods from the field of artificial intelligence (machine learning, deep learning) to extract patterns/ gain insights from large amounts of data.
Here, is a bullet point list of what I love to do:
- collaborating in cross-functional teams to solve problems
- helping others to reach their full potential
- leading people by example
- from Germany with Polish roots,
- a Managing Data Scientist at IBM,
- member of the Technical Expert Council (TEC) at IBM,
- Data Science coach and mentor,
- keynote speaker on various events such as CodeMotion Milan (usually about Generative AI)
- and a cat and dog lover.
I am happy to connect and talk to you, personally - just reach out.