Life & fun · alternatives · 2026

24 alternatives to Stochastic Gradient in Hilbert Spaces

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…

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

1
Boldly go where Gradient Descent has never gone before with DiscoGrad▲232
Trying to do gradient descent using automatic differentiation over branchy programs? Or to combine them with neural networks for end-to-end training? Then this might be interesting to you. We develped DiscoGrad, a tool for automatic differentiation through C++ programs involving input-dependent control flow (e.g., "if (f(x) < c) { ... }", differentiating wrt. x) and randomness. Our initial motivation was to enable the use of gradient descent with simulations, which often rely heavily on such discrete branching. The latter makes plain autodiff mostly useless, since it can only account for the…
2024 · github.com · its alternatives →
2
Web Gradients▲906
Curated collection of 180 free fresh background gradients 🚀
2017 · webgradients.com · its alternatives →
3
Gradienta▲292
Open-source multi-layered CSS gradients with JPG downloads
2019 · gradienta.io · its alternatives →
4
Automated smooth Nth order derivatives of noisy data▲146
This little project came about because I kept running into the same problem: cleanly differentiating sensor data before doing analysis. There are a ton of ways to solve this problem, I've always personally been a fan of using kalman filters for the job as its easy to get the double whammy of resampling/upsampling to a fixed consistent rate and also smoothing/outlier rejection. I wrote a little numpy only bayesian filtering/smoothing library recently (https://github.com/hugohadfield/bayesfilter/) so this felt like a fun and very useful first thing to…
2024 · github.com · its alternatives →
5
A weekend replication of STOKE, a stochastic superoptimiser▲58
Haskell re-implementation of STOKE, the stochastic superoptimizer - blaze/notebooks/tutorial.ipynb at master · bollu/blaze
2019 · github.com · its alternatives →
6
Stochastic – JavaScript library for simulating stochastic processes▲55
Simulation of common stochastic processes with native JavaScript - NathanEpstein/stochastic
2014 · github.com · its alternatives →
7
A radically more scalable alternative to Bayesian Optimization▲96
Experimental Global Optimization Algorithm. Contribute to chrisstroemel/Simple development by creating an account on GitHub.
2018 · github.com · its alternatives →
8TP
Tensor product analogy – functional currying▲8
Mathematical Physics & Geometric Signal Processing
2019 · jwkennington.com · its alternatives →
9
Gradient World▲104
Gradients generated from photographs of nature
2017 · its alternatives →
10
DBoost stochastic gradient boosting via Ridge with random dummies▲8
Stochastic Dummy Boosting. Contribute to mikeskim/dboost development by creating an account on GitHub.
2016 · github.com · its alternatives →
11
A perceptron with stochastic gradient descent in Go▲94
A single / multi layer / recurrent neural network written in Golang. - made2591/go-perceptron-go
2017 · github.com · its alternatives →
12OH
Optimizing Higher Order Functions with Hypothetical Inverses▲55
2016 · medium.com · its alternatives →
13
Performance evaluation of various Stable Diffusion models▲6
Comparison of different stable diffusion implementations and optimizations - fal-ai/stable-diffusion-benchmarks
2023 · github.com · its alternatives →
14
Monostate AItraining▲59
Fine-tuning, RL, and inference in one CLI
Dec 2025 · github.com · its alternatives →
15
Outpainting with Stable Diffusion on an infinite canvas▲53
Outpainting with Stable Diffusion on an infinite canvas - lkwq007/stablediffusion-infinity
2022 · github.com · its alternatives →
16
Shrinking Fractional Dimensions with Reinforcement Learning▲9
Code for CORL 2020 paper: Explicitly Encouraging Low Fractional Dimensional Trajectories Via Reinforcement Learning. - sgillen/fractal_rl
2020 · github.com · its alternatives →
17TI
The Impact of Noise on Recurrent Neural Networks▲21
Series on reservoir computing, focusing on echo state networks. Covers GPU simulations, addressing variable training lengths and data processing. Analyzes Gaussian noise impact on network performance, with practical demonstrations and theoretical discussions. Relevant for those interested in neural network behavior in noisy environments.
2023 · ampolloreno.com · its alternatives →
18
I wrote an autodiff in C++ and implemented LeNet with it▲36
2024 · gitlab.com · its alternatives →
19
The Hessian of tall-skinny networks is easy to invert▲31
It turns out the inverse of the Hessian of a deep net is easy to apply to a vector. Doing this naively takes cubically many operations in the number of layers (so impractical), but it's possible to do this in time linear in the number of layers (so very practical)! This is possible because the Hessian of a deep net has a matrix polynomial structure that factorizes nicely. The Hessian-inverse-product algorithm that takes advantage of this is similar to running backprop on a dual version of the deep net. It echoes an old idea of Pearlmutter's for computing Hessian-vector products. Maybe this…
Jan 2026 · github.com · its alternatives →
20
Modular Diffusion – A modular Python library for diffusion models▲40
Hello everyone! I've been working on this project for a few months as part of my thesis in Machine Learning. It's meant to be a library that provides an easy-to-use but flexible API to design and train Diffusion Models. I decided to make it because I wanted to quickly prototype a Diffusion Model but there were no good tools to do it with. I think it really can help people prototype their own Diffusion Models a lot faster and only in a few lines of code. The base idea is to have a Model class that takes different modules corresponding to the different aspects of the Diffusion Model process…
2023 · github.com · its alternatives →
21FS
Free Stable Diffusion 2.0 hosted interface▲25
Deploy and serve machine learnings models performantly, scalably, and cost-efficiently with Baseten.
2022 · app.baseten.co · its alternatives →
22
A reimplementation of Google's sparsehash in C▲22
A simple re-implementation of Google's sparsehash as a learning excercise. - qpfiffer/Simple-Sparsehash
2015 · github.com · its alternatives →
23
Kotlin∇: Shape-Safe Symbolic Differentiation with Algebraic Data Types▲15
🧩 Shape-Safe Symbolic Differentiation with Algebraic Data Types - breandan/kotlingrad
2021 · github.com · its alternatives →
24
Implementation of DDPM (Denoising Diffusion Probabilistic Models)▲10
Just small prototype showcasing diffusion models
2025 · github.com · its alternatives →

Also compare

Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →