Learn · Fundamentals of Generative AI and Large Language Models: Theory and Practice
Lecture 74 of 74Week 12 LLM Capabilities, Multimodal AI, Ethics
Tutorial on LLMs, Multimodal LLMs, Bias, Fairness, Evaluation and Safety
Prof. Sriram Ganapathy, Prof. Ashwini Kodipalli and Prof. Baishali Garai · IISc Bangalore PES University RV University
Credits and licence
- From
- Fundamentals of Generative AI and Large Language Models: Theory and Practice, IISc Bangalore PES University RV University (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge. Coordinated by IISc Bangalore.
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- Tweens profiles and older. How courses are placed
All the lectures
- Introduction to Generative AI
- Activation Functions & Loss Functions in Deep Learning
- Optimizers - Part A
- Optimizers - Part B
- Convolutional Neural Network - Part A
- Convolutional Neural Network - Part B
- Introduction to Google Colab
- Practical Exercise - 1
- Practical Exercise - 2
- Introduction to Autoencoder
- Training objective: Reconstruction Loss (MSE, Binary Cross-Entropy)
- Types of Autoencoders: Architecture and Code-Level Understanding
- Regularization Techniques in AE: Denoising
- Regularization Techniques in AE: Sparse
- Regularization Techniques in AE: Contractive
- Numerical Example, Limitations of AE
- Practical Exercise - 1
- Practical Exercise - 2
- Intuition behind KL Divergence - Part A
- Intuition behind KL Divergence - Part B
- Introduction to VAE and the working of the Encoder
- Working of Probabilistic Decoder, ELBO, Loss Function of VAE
- Reparameterization Trick
- Numerical Example
- Practical Exercise - 1
- Entanglement, Disentanglement Latent Space, and Beta-VAE
- Conditional VAE
- Latent Space Interpolation
- Practical Exercise - 2
- Practical Exercise - 3
- Motivation for GANs
- GAN Architecture
- GAN Objective and Loss Functions
- GAN Convergence and Nash equilibrium
- Introduction to DCGAN
- Practical Exercise - 1
- Practical Exercise - 2
- Conditional GAN
- Pix2Pix GAN
- Cycle GAN
- Style GAN
- Style GAN 2
- Practical Exercise - 1
- Introduction to Diffusion Model
- Mathematics of Diffusion Models
- "Denoising Diffusion Probabilistic Model- Forward Process"
- "Denoising Diffusion Probabilistic Model- Reverse Process"
- Hand on on forward Diffusion process
- U-Net for Denoising
- Classifier Guided Diffusion Model
- Classifier free Diffusion Models
- Stable Diffusion Model
- Hands-on on reverse diffusion process
- Foundations of Natural Language Processing
- Sequential Modeling with RNNs and LSTM
- Evolution From LSTMs to Transformers
- Transformer Architecture: Encoder
- Practical Exercise - 1
- Transformer Architecture: Decoder
- Bidirectional Encoder Representations from Transformer (BERT)
- Generative Pre-trained Transformer (GPT) Decoder-only Large Language Model
- Prompt Engineering Basics
- Hands-on on LLM
- LLMs – Recap
- In-context Learning
- Low-Rank Adaptation (LoRA)
- RAG Principle
- RAG Recent Advances
- Tutorial on In-context Learning, RAG and LoRA
- LLMs for Text Generation
- LLMs for Audio, Video and Multimodal Tasks
- Size vs Performance and Benchmarking
- Bias, Fairness and Safety
- Tutorial on LLMs, Multimodal LLMs, Bias, Fairness, Evaluation and Safety