Alternatives
Products that do what Minigrad – a small neural network lib in Golang does
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.
- 1MM
Jun 2026 · github.com
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- 3WA
In browser PPO training demo, made possible by tinygrad: TinyJit -> WebGPU kernels. Requires WebGPU.
May 2026 · ppo.gradexp.xyz
- 4IC
A friend and I wrote a book on how to build and train Deep Learning models in Go. We wanted it to be a useful reference for deep learning basics for Go programmers. Deep Learning is slowly seeping into everything we use every day and we thought it would be great if more people could do it in Go. The book is available here and on Amazon as well. https://www.packtpub.com/big-data-and-business-intelligence/hands-deep-learning-go We would appreciate any feedback and we're always looking to improve.
2019
- 5TA
Jun 2026 · github.com
- 6MA
2020 · github.com
- 7N5
2020 · nn-512.com
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- 10AL
2015 · github.com
- 11GA
I'm starting to implement a barebones version of pytorch in Go. The primary motivation is: 1. I want to better learn Pytorch and how it works so what better way than to just re-implement some of its core features. 2. I write mainly in Go and haven't come across a lot of ML support in Go 3. I'd rather have a Go ML service instead of spinning up additional infrastructure to just support a python ML service in my Go projects 4. Go's static typing, native concurrency (avoid GIL problem in python), efficient memory management, single binary deployment and more make it a better interface compared…
2024
- 12AS
I have been using golang at work and personally for more than 2 years now. A problem i faced when I started and the problem new gophers even today is how to structure the project. Most (if not all) golang tutorials seem to be very simple and do not describe how to structure a large application. Following project is an attempt to Showcase a manageable project layout for services in golang. https://github.com/spy16/droplets This project is far from complete. Would like to hear some feedback from the community before continuing on this.
2018
- 13GR
2020 · golangresources.com
- 14GT
2017 · golangbot.com
- 15GF
Hello HN, We are pleased to introduce you graphlearn-for-pytorch (https://github.com/alibaba/graphlearn-for-pytorch), an open-source distributed graph neural network library based on PyTorch and compatible with PyG. Our library is designed to make it easy for developers to build and train large-scale graph models in a distributed environment. With graphlearn-for-pytorch, you can leverage GPUs to accelerate graph sampling and utilize UVA to reduce the overheads of feature collection. Following a scalable design, graphlearn-for-pytorch supports training GNN models on…
2023 · github.com
- 16ZA
Hey HN, We’re excited to announce Zant v0.1, an open-source TinyML SDK built in Zig, designed to optimize and deploy neural networks on resource-constrained devices. Unlike existing solutions, Zant focuses on performance, portability, and ease of integration, making it a strong alternative for anyone working on Edge AI and embedded ML. Why Zant? Most TinyML frameworks are either too high-level (requiring bloated runtimes) or too low-level (requiring extensive manual optimization). Zant bridges the gap by offering: - A lightweight but powerful code generation system to translate ML models…
2025 · github.com
- 17PP
2018 · dev.to
- 18HT
2017 · made2591.github.io
- 19SG
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
- 20IM
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
- 21IW
2021 · github.com
- 22IW
I have been interested in neural nets since the 90's. I've done quite a bit of reading, but never gotten around to writing code. I used Gemini in place of Wikipedia to fill in the gaps of my knowledge. The coolest part of this was learning about dual numbers. You can see in early commits that I did not yet know about auto-diff; I was thinking I'd have to integrate a CAS library or something. Now, I'm off to play with TensorFlow.
Mar 2026 · github.com
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Build your first neural network with PyTorch step by step.
Aug 2026 · khayyamshah2007.blogspot.com
- 24GB
Apr 2026 · goblin.run
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