Alternatives
Products that do what PyTorch Neural Network Guide does
Build your first neural network with PyTorch step by step.
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Learn PyTorch from scratch with simple examples.
Aug 2026 · khayyamshah2007.blogspot.com
- 2NN
2020 · github.com
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- 5GF
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
- 6CT
2018 · github.com
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Learn how AI trains neural networks—beginner friendly.
Jul 2026 · khayyamshah2007.blogspot.com
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- 10SA
2018 · github.com
- 11RP
Feb 2026 · github.com
- 12N5
2020 · nn-512.com
- 13IM
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
- 14TR
2018 · github.com
- 15TT
2016 · github.com
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Simple AI guide for beginners
Jul 2026 · khayyamshah2007.blogspot.com
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- 18PT
2019 · github.com
- 19MA
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
- 20AV
2016 · jalammar.github.io
- 21RT
2017 · github.com
- 22PP
2025 · github.com
- 23AS
2017 · github.com
- 24GA
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
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