The classic course on Generative AI by Martin Musiol

Name: The classic course on Generative AI by Martin Musiol
Rating: 4.3 (490 reviews)

How the next milestone in machine learning will improve the products we build

Created byMartin Musiol

Last updated 5/2024

English

English [Auto],

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

Course content

11 sections • 39 lectures • 6h 1m total length

Let's get started1:28
What is Generative AI?3:16
Your Instructor Martin5:38
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.
The Course Overview6:26
Your Feedback is valuable0:52
Engage with personalized feedback for the Generative AI course by inviting a 1-to-1 discussion via LinkedIn or email, and reflect accordingly in the reviews.

Broad Application Fields and Potential3:17
GAI in Top Strategic Tech Trends4:00
Explore how generative AI sits among Gartner's top strategic tech trends, generating novel content from data and accelerating R&D in medicine and product design.
Example: Face Generation14:48
Example: Do-as-I-do-motion Transfer8:55
[Part 1] Example: GAI-generated Art AND the Interlock with Crypto & NFTs12:33
[Part 2] Example: GAI-generated Art AND the Interlock with Crypto & NFTs8:57
[Part 3] Example: GAI-generated Art AND the Interlock with Crypto & NFTs13:14
On why does Generative AI matter?

GANs are Powerful14:35
Explore how generative adversarial networks generate data and model complex distributions, learning internal representations for unsupervised learning across images, text, audio, video, and time series.
How does a GAN work?5:44
Explore how a generative adversarial network trains a generator and a discriminator in an adversarial loop, using latent seeds to produce samples and distinguishing real from fake toward Nash equilibrium.
on how GANs work
[Part 1] Excursion: Neural Networks5:59
Explore neural networks from computing graphs to multilayer networks, training with forward and backward passes and backpropagation, gradients, and supervised learning, including convolutional networks for image tasks.
[Part 2] Excursion: Neural Networks13:51
What GANs do exist? + Generative AI Ideation14:20

3D-Object Generation13:40
Generate 3d objects with a generative adversarial network and volumetric convolutional networks, exploring latent space, chairs and cars, with pix to vox for single- and multi-view reconstruction.
3D-Object Generation - Ideas12:27
on 3D-Object Generation
Interactive Image Generation8:58
[Part 1] Conditional GAN (cGAN) Demo12:05
Watch a step-by-step demonstration of a conditional gan, where labels are embedded and fused with a 100-dim latent vector to generate 28×28 handwritten digits, and the discriminator uses image-label pairs.
[Part 2] Conditional GAN (cGAN) Demo11:51
How does a GauGAN work - Ideas9:43
How to augment Data and Why9:38
What Data Augmentation Techniques exist & what is their Effectiveness12:36
Data Augmentation with a GAN - Demo11:28
Demonstrate adaptive discriminator augmentation in a generative adversarial network to generate augmented bird images from Caltech birds. Explore accuracy targets, integration steps, and evaluation metrics like inception distance and kid.
Data Augmentation: Lessons Learnt & Outlook5:38

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:

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.

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

The classic course on Generative AI by Martin Musiol

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 18min

Discriminative vs. Generative AI1 lecture • 14min

Why does Generative AI matter?7 lectures • 1hr 6min

Where is Generative AI located?2 lectures • 13min

The Power of Generative Adversarial Networks (GANs)5 lectures • 54min

Why did it take them so long?1 lecture • 11min

The implementation of a simple GAN2 lectures • 17min

A Deep-Dive into Various Application Fields10 lectures • 1hr 48min

Concerns around Generative AI Models1 lecture • 9min

Noteworthy GAN Architectures2 lectures • 24min

Requirements

Description

Who this course is for: