AI 5:

AI 5: Deep Generative Models

This course takes AI practitioners from having learnt basic AI concepts from embeddings and convolutional networks to a good understanding of generative models, especially in the context of image creation and generative style transfer. The key idea is that of a latent space which is manipulated in some fashion.

  • AI 1
  • DSE 1
  • DSE 2
  • AI 2

An understanding of Bayesian Statistics will help immensely. Having done AI3 will also help a lot as well.

  • Autoencoders
  • Basic Bayesian Statistics
  • Flow based models
  • Variational Autoencoders
  • GANs and Convolutional GANs
  • Advanced GAN models: SAGAN, CGAN and others
  • Contrastive Learning

Generative modeling is a very hot field in AI right now, as the idea is to model any arbitrary probability distribution. At he end of this course you will be able to:

  1. Implement Autoencoders and GANs
  2. Have a deep understanding of Generative Models and unsupervised+self-supervised learning and thus be able to read papers, and self-learn more advanced tasks and topics

Prerequisites

Topics

Learning Objectives

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Schedule

Students

Summer Cohort

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Conducted alongside elective

Winter Cohort

Intake 1:

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Conducted along with elective

Intake 2:

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Conducted along with elective

Working Professionals

Summer Cohort

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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Conducted along with elective

Fall Cohort

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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Conducted along with elective

Winter Cohort

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Conducted along with elective

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