NPTEL - Indian Institute of Science, Bengaluru

Lec 51 Review of Stochastic Approximation Concepts

Lec 50 Review of Probability Theory and Fundamental Inequalities

Lec 49 Analysis of Limiting Dynamics in Q-Learning with Function Approximation

Lec 48 Q-Learning with Linear Function Approximation under \epsilon-Greedy Exploration

Lec 47 Q-Learning with Linear Function Approximation — A Unified Switching Systems Perspective

Lec 46 Application to Materials Science

Lec 45 Application to Materials Science

Lec 44 Application to Materials Science

Lec 43 Detection of electrons and ECCI

Lec 42 Image Formation

Lec 41 Defect analysis

Lec 40 Imaging

Lec 39 Diffraction Patterns

Lec 38 Electron Diffraction

Lec 37 TEM column cross-section

Lec 36 Electron specimen interaction

Lec 65 Neural Networks with Tensorflow (Tutorial II)

Lec 64 Neural Networks with Tensorflow (Tutorial I)

Lec 63 Variational Autoencoders and Bayesian Generative Modeling

Lec 62 Graph Neural Networks and Generative AI Fundamentals

Lec 61 Recurrent Neural Networks and Sequential Data Processing

Lec 60 Neural Network Challenges (Gradients, Overfitting) and Logic Gate Implementation

Lec 59 Challenges in Training Neural Networks and their Mitigation

Lec 58 Training an Artificial Neural Network:Forward Propagation,Backpropagation,and Hyperparameters

Lec 57 Mathematical Foundation and Activation Functions of Neural Networks

Lec 56 Overview of Advanced Neural Network Architectures: From CNNs to GANs and GNNs

Lec 46 Concluding the Asymptotic Analysis of Q-Learning

Lec 45 Asymptotic Behaviour of the Q-Learning Limit ODE — A Switching Systems Perspective

Lec 44 Asymptotic Analysis of Q-Learning Algorithm

Lec 43 Best Policy Algorithm for Q-Value Functions: A Stochastic Approximation Formulation