Stochastic Gradient Descent: Theory, Numerical Examples, and Implementation
A comprehensive guide to Stochastic Gradient Descent (SGD), covering mathematical foundations, variance analysis, convergence theory, numerical step-by-step ...
A comprehensive guide to Stochastic Gradient Descent (SGD), covering mathematical foundations, variance analysis, convergence theory, numerical step-by-step ...
Explore the mathematical principles behind contrastive loss, InfoNCE, and supervised contrastive learning.
A derivation of binary cross entropy to showcase its mathematical foundation
KL Divergence (Dipesh Dai) Limitations of Autoencoder (Suruchi) Variational Autoencoder Basic Architecture of Variational Autoencoder (Rajan) Loss of VAE...
Autoencoder Difference between generative and discriminative modelling Generative modelling Generative modelling is a statistical modeling technique that ...