Admissions Open for JANUARY Batch
Understand VAEs and GANs for creating new data from mathematical models.
Days : Tue & Thu
Duration : 3 Hours
Timings: 8 - 10 PM IST
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3 Hours
9 - 10 PM IST
Tue & Thu
Maths for Generative Models
Understand VAEs and GANs for creating new data from mathematical models.
Online Live Instructor-Led Learning
3 Hours
8 - 10 PM IST
Tue & Thu
By end of this course
Get stronger in
Variational Autoencoder loss functions
GAN training and discriminator math
Get familier with
Latent space representation
Optimal transport concepts in generation
New Batch Starts : jan 2026
Limited seats only 15 students per batch
Who Should Enroll?
This course is for learners aiming to understand the mathematical foundations of generative modeling, including probability distributions, latent spaces, and optimization, tailored for those pursuing domain expertise in generative AI.
Prerequisites
Probability, optimization, and deep learning math.
Experience our course risk-free
We offer a 15-day money back guarantee
Prerequisite
Probability, optimization, and deep learning math.
Who Should Enroll?
This course is for learners aiming to understand the mathematical foundations of generative modeling, including probability distributions, latent spaces, and optimization, tailored for those pursuing domain expertise in generative AI.
By the end of this course
Get Stronger in
- Variational Autoencoder loss functions
- GAN training and discriminator math
Get Familiar in
- Latent space representation
- Optimal transport concepts in generation
What you will learn
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Course Contents
Topics
- Latent variable models
- Evidence Lower Bound (ELBO) derivation
- Reparameterization trick math
Key Outcomes
Understand and implement VAEs
Topics
- Min–max optimization formulation
- Jensen-Shannon divergence math
- Gradient dynamics in adversarial training
Key Outcomes
Build and optimize GAN architectures
Topics
- Latent variable models
- Evidence Lower Bound (ELBO) derivation
- Reparameterization trick math
Key Outcomes
Understand and implement VAEs
Topics
- Latent variable models
- Evidence Lower Bound (ELBO) derivation
- Reparameterization trick math
Key Outcomes
Understand and implement VAEs
Topics
- Min–max optimization formulation
- Jensen-Shannon divergence math
- Gradient dynamics in adversarial training
Key Outcomes
Build and optimize GAN architectures
Topics
- Latent variable models
- Evidence Lower Bound (ELBO) derivation
- Reparameterization trick math
Key Outcomes
Understand and implement VAEs