"Generative AI", Trustable-AI" and "Self-Supervised Learning"

Generative AI

Content: 

  • Part-1
    • Basics of Autoencoders
    • Basics of Variational Autoencoders
    • Why Variational Autoencoder works like Generative Model but Traditional Autoencoder Not?
    • Step-by-Step discussion on Variational Autoencoder.
  • Part-2.
    • Variational Autoencoder
    • Variational Inference
    • Computing - Cost function and other Mathematical formulations

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Content: 

  • Part-1
    • Basics of GAN
    • Function and Technique behind:
      • Generator Model
      • Discriminator Model
      • GAN Architecture
    • GAN Loss Function
  • Part-2.
    • Deep Convolutional GAN (DCGAN)
    • GAN key issues in Detail
      • Mode Collapse Problem
      • Training Instability
      • Overfitting Issues
    • Convolutional Up sampling 
    • Convolutional Down sampling
  • Part-3 
    • Step-By-Step Process to write the code for DCGAN.
    • Detailed explanation behind each components of the code.

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Content: 

  • Part-1
    • Basics of Wasserstein GAN.
    • Why we need Wasserstein Distance?
    • Difference between – (a) KL-Divergence, Vs (b) Jensen-Shannon Divergence Vs (c) Wasserstein Distance
  • Part-2.
    • Wasserstein Distance.
    • Wasserstein Distance through -  (a) Kantrovich-Rubinstein Duality and (b) 1-Lipschitz Function
  • Part-3 
    • Wasserstein GAN Architecture.
    • Wasserstein GAN KERAs implementation

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Self-Supervised Learning

Content: 

  • Basics of Self-Supervised Algorithm 
  • Details of Deep Clustering
  • Details of Cost Functions used in the Deep Clustering Algorithms 
.

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Content: 

  • Basics of L2-Norm 
  • Basics of Unit-Sphere 
  • Why we use both in Contrastive /Self-Supervised Learning. 
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3. Self-Supervised Online Clustering (Unsupervised Learning of Visual Features).

Content: 

  • Basics of Contrastive Learning 
  • Paper Discussion: “Unsupervised Learning of Visual Features by Contrastive Cluster Assignments” 
.

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Trustable AI

Content: 

  • Basics of Trustable AI 
  • Uncertainty Estimation 
  • Aleatoric Uncertainty 
  • Epistemic Uncertainty 
  • Model Reliability Model Calibration 
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