Bookmarks
How to get from high school math to cutting-edge ML/AI: a detailed 4-stage roadmap with links to the best learning resources that I’m aware of.
1) Foundational math. 2) Classical machine learning. 3) Deep learning. 4) Cutting-edge machine learning.
Iterative α-(de)Blending: a Minimalist Deterministic Diffusion Model
The paper presents a simple and effective denoising-diffusion model called Iterative α-(de)Blending. It offers a user-friendly alternative to complex theories, making it accessible with basic calculus and probability knowledge. By iteratively blending and deblending samples, the model converges to a deterministic mapping, showing promising results in computer graphics applications.
Pattern Recognition and Machine Learning
The content discusses likelihood functions for Gaussian distributions, maximizing parameters using observed data, Bayesian model comparison, mixture density networks, and EM algorithm for Gaussian mixtures. It covers topics like posterior distributions, predictive distributions, graphical models, and variational inference. The material emphasizes probability distributions, optimization, and model comparison.
Probability and InformationTheory
In this chapter, the authors discuss probability theory and information theory. Probability theory is a mathematical framework for representing uncertain statements and is used in artificial intelligence for reasoning. Information theory, on the other hand, quantifies the amount of uncertainty in a probability distribution. The chapter explains various concepts, such as probability mass functions for discrete variables and probability density functions for continuous variables. It also introduces key ideas from information theory, such as entropy and mutual information. The authors provide examples and explanations to help readers understand these concepts.
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