Generative Models: Crash Course

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 2 of 4

Wireless Receivers: Time and Phase Offset

Covers the impact and compensation of time and phase offset in wireless receivers.

Digital Signal Processing: Theory

Covers the theory of digital signal processing, including sampling, transformation methods, digitization, and PID controllers.

Powder Characterisation: Physical, Chemical, Morphology

Explores the physical, chemical, and morphological analysis of powders, emphasizing sampling accuracy and quality control.

Explicit Stabilised Methods: Applications to Bayesian Inverse Problems

Explores explicit stabilised Runge-Kutta methods and their application to Bayesian inverse problems, covering optimization, sampling, and numerical experiments.

Generative Models: Self-Attention and Transformers

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

Monte Carlo Markov Chains

Covers the theory of Markov chains and Monte Carlo methods.

Sampling: Signal Reconstruction and Aliasing

Covers the importance of sampling, signal reconstruction, and aliasing in digital representation.

Signals & Systems I: Sampling and Reconstruction

Explores ideal sampling, Fourier transformation, spectral repetition, and analog signal reconstruction.

Sampling: DT-time processing of CT signals

Covers the importance of sampling in signal processing, including the sampling theorem and signal reconstruction.

Generative Models: Boltzmann Machine

Covers generative models, focusing on Boltzmann machines and constrained maximization using Lagrange multipliers.