books.txt

https://services.math.duke.edu/~rtd/EOSP/EOSP2021.pdf - stochastic processes
https://arxiv.org/abs/2106.10165 - principles of deep learning theory
https://arxiv.org/abs/2104.13478 - geometric deep learning
https://hastie.su.domains/ElemStatLearn/printings/ESLII_print12_toc.pdf
https://ia601508.us.archive.org/35/items/collection-of-mathematics-probability-and-statistics-books/A%20First%20Course%20in%20Probability%2C%20Global%20Edition%20%28Sheldon%20Ross%29.pdf
https://www.cs.uoi.gr/~arly/courses/ml/tmp/Bishop_book.pdf - PRML, Chris Bishop
https://github.com/probml/pml2-book/releases/latest/download/book2.pdf
https://maurice-weiler.gitlab.io/cnn_book/EquivariantAndCoordinateIndependentCNNs.pdf
https://arxiv.org/abs/1504.03001 - Chaos on the interval (225pp)
https://www.ma.imperial.ac.uk/~dturaev/kuznetsov.pdf - Elements of Applied Bifurcation Theory (614pp)
https://arxiv.org/abs/2110.01765 - deep kernel shaping (172pp)
https://www.inference.org.uk/itprnn/book.pdf - Information Theory, Inference, and Learning Algorithms (640pp)
https://arxiv.org/abs/1412.1193 - Perspectives on Natural Gradient (72pp)
https://arxiv.org/abs/1906.10652 - Monte Carlo Gradient Estimation (62pp)
https://arxiv.org/pdf/2406.08929 - Step-by-Step Diffusion: An Elementary Tutorial (51pp)
https://arxiv.org/pdf/2304.12482 - Information Theory for Complex Systems Scientists (105pp)
https://www.cs.purdue.edu/homes/tamaldey/book/CTDAbook/CTDAbook.pdf - Computational Topology for Data Analysis (377pp)
https://arxiv.org/pdf/2204.02909 - Mean-Field Spin Glass Techniques for Non-Physicists (171pp)
https://www.di.ens.fr/%7Efbach/ltfp_book.pdf - Learning Theory from First Principles (488p)
https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf - Convex Optimization (714pp)
https://arxiv.org/pdf/2412.05265 - Kevin Murphy RL overview (144pp)
http://incompleteideas.net/book/RLbook2020.pdf - Barto and Sutton RL (548pp)
https://arxiv.org/pdf/1803.00567 - Gabriel Peyre - Computational Optimal Transport (209pp)