My first introduction to probability theory came from Bertsekas textbook. After I later absorbed the measure theoretic formalism of the subject, I still find myself occasionally revisiting the book for practical computation techniques. Later, when I worked as a quant researcher in finance, I learned the theory of model predictive control from his book βDynamic programming and stochastic controlsβ. The idea of open loop feedback control motives me to write the paper https://t.co/VFLfVaCO1k where we formulate multi-step time series forecasting as a model predictive control problem.
These days, the RL in the LLM context feels more like contextual bandit with policy gradient methods. For example, the usual notation in this context is pi(x|y). There is also rich control theoretic formulation of RL with known dynamics or joint estimation of the dynamics that I have learned from Bertsekas textbook.
Both books are still on my desk side bookshelf. I often find Bertsekas is able to design a notation system thatβs optimized for understanding and calculation. He introduces many meaningful intermediate abstractions that end up being more tractable objects to reason about. Rest in peace, Professor Bertsekas!
@lanandira@fachtomi@Rakarsnd_@SpieltagIndo Average loser mentality, blame the other and also compare their player with the player that not in their prime era. You just lucky that your team not get washed by PSG because Arteta put the whole Arsenal team in penalty boxes π€£π€£π€£π€£π€£π€£π€£.
"Matrix Calculus for Machine Learning and Beyond" is an interesting set of free lecture notes for understanding the mathematics behind modern deep learning. It covers gradients, Jacobians, Hessians, matrix-valued functions, backpropagation, optimisation, and many of the mathematical structures used in machine learning and AI models.
One interesting aspect is that the material maintains a strong university-level rigour while remaining highly visual: the notes include numerous diagrams, graphs, geometric interpretations, and intuitive explanations of matrix calculus applied to neural networks.
It is a valuable resource not only for students studying machine learning, but also for anyone who wants to build a solid foundation in computational linear algebra and optimisation.
https://t.co/XsnReRHu35
MIT's "The Mathematics in Toys and Games"
Explores the mathematical strategies behind popular games, toys, and puzzles.
Lecture Notes: https://t.co/RnclMR0bKC
Prabowo Subianto is centralising power, marginalising opposition and spending beyond Indonesiaβs means. He could undo 20 years of economic and political progress https://t.co/f80KbNbU6l
Prabowo Subianto is centralising power, marginalising opposition and spending beyond Indonesiaβs means. He could undo 20 years of economic and political progress https://t.co/XiM5hgMhyi
Photo: Reuters