Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.
Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.
Covers the binary choice model, error term assumptions, specific constants, invariances, and distribution properties.
Explores the regulation of therapeutic products, covering legal requirements, product classification, market access, and advertising rules.
Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.
Explores extreme events, emission metrics, and the Paris Agreement's goals, analyzing the impact of different metrics on temperature outcomes and the challenges in defining extreme events.
Covers the concept of compression and constructing prefix-free codes based on given information.
Covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones.
Explores decentralized storage & distribution using BitTorrent & IPFS, covering goals, challenges, building specifications, integrity, bootstrapping, performance, incentives, versioning, naming, and CRDTs.
Covers concentration inequalities and sampling methods for estimating unknown distributions, with a focus on population infection rates.
