Spatial Sampling: Concepts and Techniques

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Related lectures (30)

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

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.

Graph Coloring: Theory and Applications

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

Deep Learning Modus Operandi

Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.

Natural Language Generation: Decoding & Training

Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.

Generative Models: Self-Attention and Transformers

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

Frequency Sampling

Explores the frequency sampling method to approximate ideal filters, useful for quick prototyping but lacking fine control over errors.

Digital Elevation Models: Basic Concepts

Covers methods for sampling elevation and measuring elevation using leveling, photogrammetry, and LiDAR in Geographic Information Systems.

Fourier Transform and Sampling

Covers the Fourier transform of sampled signals, reconstruction, and harmonic response.

Generative Models: Boltzmann Machine

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