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Audio AI Researcher & Developer
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Module 5
Foundations of Generative Modeling
1
Understanding GANs: Generator, Discriminator, and Adversarial Loss
Explain the core principles of Generative Adversarial Networks (GANs), including the generator, discriminator, and adversarial loss.
2
DCGAN Architecture and Training Objectives
Describe the architectural components and training objective of a Deep Convolutional GAN (DCGAN).
3
Understanding VAEs: Architecture, Objective, and the Reparameterization Trick
Explain the architecture and objective function of a Variational Autoencoder (VAE), including the reparameterization trick.
4
GANs vs. VAEs: Understanding Latent Spaces
Distinguish between the latent spaces learned by GANs and VAEs.
5
Introduction to Normalizing Flows
Describe the concept of Normalizing Flows for constructing complex probability distributions from simple ones.
6
Normalizing Flows: The Power of Invertible Transformations
Explain how invertible transformations are used in Normalizing Flow models.
7
GANs, VAEs, and Flow-based Models: A Comparative Analysis
Compare and contrast the characteristics of GANs, VAEs, and Flow-based models in terms of sample quality, diversity, and training stability.
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Sequence Modeling with Transformers
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Supervised Speech Recognition Models