Generative Adversarial Networks (GANs, Overview)

Summary

Two networks (generator and discriminator) trained adversarially so the generator learns to produce realistic synthetic data — a historically important generative approach, now largely eclipsed by diffusion models for images.

How to Learn This

1Learn the generator-vs-discriminator training loop conceptually.
2Understand why GANs are notoriously hard to train (mode collapse, instability).
3Read about how diffusion models have largely replaced GANs for image generation.

Autoencoders

Text Preprocessing (Tokenization, Stemming, Lemmatization)

More topics in Deep Learning Architectures

Convolutional Neural Networks (CNNs)Pooling Layers Recurrent Neural Networks (RNNs)LSTMs & GRUs Sequence-to-Sequence Models Attention Mechanism Transformer Architecture Positional Encoding Transfer Learning Autoencoders

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