First online course on Generative Artificial Intelligence
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Build an AI that generates images, videos, music, etc. that you like. For example, describe a scene and the AI generates an image that fits best.
Do you lack data for a better performance of your applications? Then learn here how to improve your data set, having the right variance and purity.
Build an AI that disrupts (physical) product design. For example, when designing a chair add and subtract features, e.g. armrests as needed.
The topic of artificial intelligence is moving fast. We will keep you up-to-date for a long time, as it recaps what research has accomplished and where it is going.
Improve your dataset for a better AI performance.
Photo-realistic images from a descriptive text only.
Better product design: 3D objects out of images, text, etc. Further, add/subtract features (e.g. armrests) as it is best.
Style-transformations on Images or Video.
This course provides you all relevant concepts of Generative AI. It is structured in the following way:
Introduction + AI Overview
Generative AI and core concepts
First, we will introduce the broad topic of artificial intelligence (AI), what it exactly is, and what its fundamental subfields are - such as Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Natural Language Processing (NLP), etc. Furthermore, a short introduction to the programming language Python and helpful libraries such as TensorFlow will be provided optionally. In the next part we dive deep into Generative AI. This part of the course is going to be structured in application modules that are rich with examples. Each module elaborates on what it is, where it brings value, and how you can build such an AI yourself.
The course is in English. We are dealing with concepts that achieve top performance results. Lastly, one of our top priorities is it to have visually appealing examples throughout the course.
ML - Machine Learning
DL - Deep Learning
RL - Reinforcement Learning
NLP - Natural Language Processing
GAI - Generative AI
Programming (short & sweet)
TensorFlow, Keras, etc.
Git Repo and Project Administration
Generative AI and its core algorithms
GANs - Generative Adverserial Networks
Autoencoder (Universal Neural Style-Transfer)
VAEs - Variational Autoencoders
RNNs - Recurrent Neural Networks
Application Modules (incl. real-world examples and how to implement it)
Interactive Image Generation
Domain-transfer (i.e. image style-transfer, sketch-to-image)
Synthetic Data Generation
Automatic Video Generation
3D Object Generation
Cybersecurity 2.0 (Adversarial Defense vs. Attack)
[Bonus] Super Resolution Images
[Bonus] Generative Art
[Bonus] Human Pose Estimation
How to read research paper efficiently?
Strategy on how to develop your own application using Generative AI