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Related lectures (32)

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Generative Models: Self-Attention and Transformers

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

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.

Fourier Transform and Sampling

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

Markov Chains and Algorithm Applications

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

Sampling: Signal Reconstruction and Aliasing

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

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.

Signals & Systems I: Sampling and Reconstruction

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

Natural Language Generation: Decoding & Training

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

Sampling: DT-time processing of CT signals

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