Alternatives

Products that do what Stochastic Gradient in Hilbert Spaces does

I wrote a free, proof-complete monograph on stochastic gradient methods in infinite-dimensional Hilbert spaces. It starts from the functional-analysis foundations (Riesz, Radon–Nikodym), gives five equivalent definitions of stochastic gradients, proves existence/uniqueness of the dynamics, connects the continuum limit to gradient-flow PDEs, and derives explicit convergence rates under multiple assumption sets (convex, strongly convex, PL/KL, heavy-tailed noise, proximal). It also covers special cases (Gaussian/RKHS), extends to Hilbert manifolds/Banach spaces, analyzes…

1. 1G
   GradientMagic▲259

   2020 · gradientmagic.com

2. 2SJ
   Stochastic – JavaScript library for simulating stochastic processes▲55

   2014 · github.com

3. 3
   Gradienta▲292

   Open-source multi-layered CSS gradients with JPG downloads

   2019

4. 4AR
   A radically more scalable alternative to Bayesian Optimization▲96

   2018 · github.com

5. 5PA
   Pykov – a tiny Python module on finite regular Markov chains▲37

   2014 · github.com

6. 6SA
   Solid – A comprehensive gradient-free optimization framework in Python▲87

   2017 · github.com

7. 7
   Rayst Gradients▲179

   A collection of beautiful gradients generated by AI

   2022

8. 8
   Gradient World▲104

   Gradients generated from photographs of nature

   2017

9. 9
   Stable Diffusion Reimagine▲153

   Creates infinite variations for your images

   2023

10. 10FP
    Floptimal – Preflop ranges reimagined▲135

    2021 · floptimal.com

11. 11EA
    Eole, a Lévy-optimal lambda calculus evaluator written in Rust▲106

    2019 · github.com

12. 12
    Monostate AItraining▲59

    Fine-tuning, RL, and inference in one CLI

    Dec 2025

13. 13AW
    A weekend replication of STOKE, a stochastic superoptimiser▲58

    2019 · github.com

14. 14TP
    Tensor product analogy – functional currying▲8

    2019 · jwkennington.com

15. 15DS
    DBoost stochastic gradient boosting via Ridge with random dummies▲8

    2016 · github.com

16. 16MA
    Markov – A compact C++ library to easily simulate Markov processes▲14

    2015 · github.com

17. 17PE
    Performance evaluation of various Stable Diffusion models▲6

    2023 · github.com

18. 18AR
    A Rigorous Proofless Approach to Linear Algebra [pdf]▲14

    2017 · github.com

19. 19LM
    Living Memory Dynamics – "living" episodic memory embedding space▲6

    I built Living Memory Dynamics (LMD), a Python framework for simulating biologically-inspired "living" episodic memory directly in embedding space—no external LLM required for the core dynamics. Memories evolve over time like living entities: they have metabolic energy states (vivid → active → dormant → fading → ghost), emotional trajectories, and resonance fields that let them influence each other. The central piece is a new differential equation I derived (the Joshua R. Thomas Memory Equation) that drives continuous-time evolution: dM/dt = ∇φ(N) + Σⱼ Γᵢⱼ R(vᵢ, vⱼ) + A(M, ξ) + κη(t)…

    Jan 2026 · github.com

20. 20TH
    Tron Hilbert Curve Macro▲5

    is it useful? probably not!

    Apr 2026 · github.com

21. 21PV
    Pluggable vector space models▲12

    2019 · github.com

22. 22E0
    Expreduce 0.2, a free Mathematica-compatible CAS written in Go▲14

    2017 · github.com

23. 23DA
    DynamiCrafter: Animating Open-Domain Images with Video Diffusion Priors▲6

    Hello HN! We have released a major update of our image-to-video diffusion model, DynamiCrafter, with better dynamic, higher resolution, and stronger coherence. DynamiCrafter can animate open-domain still images based on text prompt by leveraging the pre-trained video diffusion priors. Please check our project page and paper for more information. We will continue to improve the model's performance. Comparisons with Stable Video Diffusion and PikaLabs can be found at https://www.youtube.com/watch?v=0NfmIsNAg-g Online demo:…

    2024 · github.com

24. 24SA
    Student attempt at proving P ≠ NP using geometry and lattices▲6

    Hi everyone, I’m a student with a strong interest in computer science and complexity theory. Recently, I worked on a manuscript attempting to prove that P ≠ NP. I know how this sounds — it’s one of the hardest and most debated problems in CS, and many have tried and failed. I don’t claim to have the final answer, but I believe the approach I used might at least offer some fresh perspective or provoke useful critique. The idea involves geometric separation between deterministic and nondeterministic computation, using high-dimensional lattice constructions and some physics-inspired intuition.…

    2025 · zenodo.org

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