Alternatives
Products that do what Boldly go where Gradient Descent has never gone before with DiscoGrad does
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…
- 1MM
Jun 2026 · github.com
- 2GGradientMagic▲259
2020 · gradientmagic.com
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2017 · craftinginterpreters.com
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2015 · ruarai.github.io
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2017 · github.com
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- 10MA
I built a neural network library in golang on an autograd engine. Faster and easy to use like PyTorch. Please give it a try and share your feedback. if you like it, a github star will be appreciated.
2024 · github.com
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2017 · github.com
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2019 · gradient.academy
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Hi guys, I’m excited to share an update on ReproModel, an open-source toolbox designed to streamline the testing and reproduction of machine learning models. I, like many of you, have really struggled with benchmarking and comparing models, from missing code, to opaque experiment parameters slowing the process. I decided to take matters into my own hands, and created a mini-toolbox in my free time to streamline the process. The goal is to reduce the time and effort spent on replicating experiments, enabling researchers to focus on innovation rather than setup. Knowing this task is not an…
2024 · github.com
- 16OA
https://github.com/gugarosa/opytimizer Did you ever reach a bottleneck in your computational experiments? Are you tired of selecting suitable parameters for a chosen technique? If yes, Opytimizer is the real deal! This package provides an easy-to-go implementation of meta-heuristic optimizations. From agents to search space, from internal functions to external communication, we will foster all research related to optimizing stuff. Use Opytimizer if you need a library or wish to: - Create your optimization algorithm; - Design or use pre-loaded optimization tasks; -…
2021
- 17VA
VibeSolve is an open-source tool that turns a plain-English description of an optimisation problem into Timefold code. Mathematical optimisation is a branch of mathematics and computer science that searches for the minimum/maximum of objective functions, and has applications in transport, logistics, scheduling etc. We are exploring where LLMs can add value in optimisation algorithm development, and where they get in the way. Right now, it works well for rapid prototyping. It does not create production-ready code and requires technical skills to use. It is noticeably better at creating…
Jun 2026 · vibesolve.ai
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After six months of work, I am here again presenting Fluent – a tiny lang which is optimized for differentiable & reactive programming. Since I am not Conal Elliot, don't expect a beautiful theoretical unification of FRP and AD from first principles. Rather, a horrific monster that holds together mostly because a lot of duct-tape. The link points to the semi-interactive tour of the language, which will get the job done much better than I could in here. Hope you hate/like it!
Jul 2026 · mlajtos.github.io
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2020 · github.com
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I’m 15 and self-taught. I'm learning ML from scratch because I want to really understand how things work. I’m not into frameworks. I prefer math, logic, and C++. I implemented a basic MLP that supports different activation and loss functions. It was trained via mini-batch gradient descent. I wrote it from scratch, using no external libraries except Eigen (for linear algebra). I learned how a Neural Network learns (all the math) -- how the forward pass works, and how learning via backpropagation works. How to convert all that math into code. I’ll write a blog soon explaining how MLPs work in…
2025 · github.com
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Hi HN, I built NOMA (Neural-Oriented Machine Architecture), a systems language where reverse-mode autodiff is a compiler pass (lowered to LLVM IR). My goal is to treat model parameters as explicit, growable memory buffers. Since NOMA compiles to standalone native binaries (no Python runtime), it allows using realloc on weights mid-training. This makes "self-growing" architectures a system primitive rather than a complex framework hack. I just pushed a reproducible benchmark (Self-Growing XOR) to validate the methodology: it compares NOMA against PyTorch and C++, specifically testing how…
Dec 2025 · github.com
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2016 · gradient-pricing.herokuapp.com
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Hello HN, I recently posted a work-in-progress paper, along with code necessary for replicating all its results, at: https://github.com/glassroom/heinsen_routing Among other things, the code in this repo outperforms Hinton et al.'s recent state-of-the-art result in visual recognition[0] while requiring fewer parameters and an order-of-magnitude fewer training epochs. Most of the original research we do at work tends to be either proprietary in nature or tightly coupled to internal code, so we cannot share it with the world. In this case, however, I was able to remove all…
2019
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Hey HN! Really excited to share this new image de-identifier. Kind of like a game of image telephone, you can upload an image and our app will convert the image to a caption, then will generate an image using stable diffusion using the parameters you set for the model yourself. We had so much fun making this and seeing the varying levels of scary generated images.
2023 · imafake.tonic.ai
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