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4.25
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Generative AI – From Big Picture, to Idea, to Implementation

How the next milestone in machine learning will improve the products we build
Instructor:
Martin Musiol
947 estudiantes matriculados
English [Auto]
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

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: 

  1. 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.

  2. 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.

  3. 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.

Introduction

1
Let's get started
2
What is Generative AI?
3
Your Instructor Martin

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.


4
The Course Overview
5
Your Feedback is valuable

Discriminative vs. Generative AI

1
Discriminative vs. Generative AI
2
On discriminative vs. generative AI.

Why does Generative AI matter?

1
Broad Application Fields and Potential
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GAI in Top Strategic Tech Trends
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Example: Face Generation
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Example: Do-as-I-do-motion Transfer
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[Part 1] Example: GAI-generated Art AND the Interlock with Crypto & NFTs
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[Part 2] Example: GAI-generated Art AND the Interlock with Crypto & NFTs
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[Part 3] Example: GAI-generated Art AND the Interlock with Crypto & NFTs
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On why does Generative AI matter?

Where is Generative AI located?

1
Where is GAI located?
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The Evolution of Deep Generative Models
3
On The Evolution of Deep Generative Models

The Power of Generative Adversarial Networks (GANs)

1
GANs are Powerful
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How does a GAN work?
3
on how GANs work
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[Part 1] Excursion: Neural Networks
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[Part 2] Excursion: Neural Networks
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What GANs do exist? + Generative AI Ideation

Why did it take them so long?

1
Why did it take them so long?

The implementation of a simple GAN

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[Part 1] GAN Demo
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[Part 2] GAN Demo

A Deep-Dive into Various Application Fields

1
3D-Object Generation
2
3D-Object Generation - Ideas
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on 3D-Object Generation
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Interactive Image Generation
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[Part 1] Conditional GAN (cGAN) Demo
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[Part 2] Conditional GAN (cGAN) Demo
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How does a GauGAN work - Ideas
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How to augment Data and Why
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What Data Augmentation Techniques exist & what is their Effectiveness
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Data Augmentation with a GAN - Demo
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Data Augmentation: Lessons Learnt & Outlook

Concerns around Generative AI Models

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Concerns around Generative AI Models

Noteworthy GAN Architectures

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[Part 1] Noteworthy GAN Architectures
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[Part 2] Noteworthy GAN Architectures

GAI Applications and further Ideas

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GAI can learn to simulate full Games & how to remaster Games
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[Part 1] Text Generation with (NLP) Language Models
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[Part 2] Text Generation with (NLP) Language Models

Thank you so much for taking the course. I would highly appreciate connecting with you on LinkedIn.

To find me: I am the only Martin Musiol in Munich (As far as I know.)


Thank you!

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Incluye

6 horas de video a pedido
Acceso completo de por vida
Acceso en el móvil y en la televisión
Certificado de finalización
Generative AI – From Big Picture, to Idea, to Implementation
Precio:
$84.99 $15
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