Artificial Intelligence

Generative AI Fundamentals: From Autoencoders to GANs

A free learning path covering core generative AI concepts, model families, and hands-on implementations using TensorFlow and Python.

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Lessons: 0
Level: intermediate

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About this path

This learning path is built from the 'Generative AI - A Complete Course' playlist by Datafuse Analytics. It guides you through the essentials of generative AI, starting with simple explanations and model families, then moving into practical tutorials on autoencoders, variational autoencoders, and GANs. You'll also explore real-world considerations like GAN limitations and a look at Google's Gemma model. By the end, you'll have a solid foundation in how these models work and how to implement them.

What you will learn

Explain the core concepts of generative AI and modeling.
Identify different families of generative models and their use cases.
Implement autoencoders and variational autoencoders from scratch in TensorFlow Keras.
Understand GANs through intuitive examples and implement a DCGAN.
Recognize the limitations and ethical considerations of GANs.
Explore a real-world application of a modern language model (Gemma).

Who this is for

Self-paced learners who want a clear free path with creator-credited YouTube lessons.

Curriculum

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