sampling

Michael Betancourt's lecture at MLSS Iceland 2014 on efficient Bayesian inference with Hamiltonian Monte Carlo. It covers MCMC concepts and motivation and the problems of sampling in high-dimensional distributions.

David Kriesel's 31C3 talk, in German, on the discovery that Xerox scanner-copiers silently replace digits and letters in scanned documents. He explains how the bug stayed unnoticed in the wild for years.

The first lecture of David MacKay's Cambridge course on information theory, pattern recognition and neural networks. It introduces the core ideas of his book Information Theory, Inference, and Learning Algorithms.

Timur Doumler's CppCon 2015 talk on how C++ is used in the audio industry. It covers the patterns and tools audio developers rely on, including handling real-time constraints.

Edward Kmett's 2013 talk at Prezi in Budapest on succinct data structures, which store data in close to the information-theoretic minimum space while supporting fast queries. It ties together the research and walks through his Haskell succinct library.

Lex Fridman interviews Marcus Hutter of DeepMind about universal artificial intelligence, AIXI, Kolmogorov complexity, Solomonoff induction and the idea of intelligence as compression.

A 105-minute technical deep dive from Noise In Space evaluating whether Starlink satellites could work as synthetic aperture radar. It covers SAR signal-to-noise, amplifiers, antennas, duplexing, phased array beamforming and simulations of V2 Mini and V3 satellites.

The first lecture of Stanford's Natural Language Processing with Deep Learning course, taught by Chris Manning and Richard Socher. It introduces NLP and word vectors, including SVD, skip-gram, CBOW, negative sampling and word2vec.

Andrej Karpathy builds a bigram character-level language model from scratch, introducing torch.Tensor and the framework of training, sampling and loss evaluation. It is the first step toward a GPT-style Transformer in his makemore series.

Scott Wlaschin's NDC Oslo talk on four 1970s languages, SQL, Prolog, ML and Smalltalk. It explains their philosophies and what practical principles they still offer modern developers.