Lectures related to Deep belief network

Covers document analysis, topic modeling, and deep generative models, including autoencoders and GANs.

Covers deep generative models, including LDA, autoencoders, GANs, and DCGANs.

Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.

Quantum State Tomography: Neural Network Approach

Explores a neural network approach to quantum state tomography using RBM, showcasing accurate predictions and potential applications beyond RBMs.

Generative Models: Self-Attention and Transformers

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Deep Visual Recognition: Interpretability

Explores deep visual recognition, interpretability, CNN architectures, visual dictionaries, and attention mechanisms.