AI · alternatives · 2026

24 alternatives to Reimplementing PyTorch from scratch (MLP, CNN) to learn the internals

Implement classic neural networks from scratch without using nn.Module, to deeply understand PyTorch and model principles. - geyuxu/nn-from-scratch

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 1 we have checked on their own sites, 1 of them free or with a free tier.

1
I built a deep learning engine from scratch in Python▲30
I’ve spent the last few months building a deep learning engine completely from scratch in Python (using only math and random). What started as a basic linear algebra calculator project grew into a symbolic tensor system with autodiff, custom matrix ops, attention mechanisms, LayerNorm, GELU, and even a text generation demo trained on the Brown corpus. I'm still an undergrad, so my main goal is to deeply understand how deep learning actually works under the hood - gradients, attention, backpropagation, optimizers - by building it step-by-step with full visibility into everything, and without…
2025 · github.com · its alternatives →
2
Netmind Power▲272
Hi HN, We just opened public access to Qwen3‑Coder‑480B‑A35B‑Instruct via a REST/SDK API. Why it might be interesting: - Fully open-source (Apache‑2.0): https://github.com/QwenLM/Qwen3-Coder - 480B params (35B active MoE), long context up to 1M tokens with YaRN - Matches Claude Sonnet/GPT‑4o on SWE-Bench Verified (81.6%) - Free tier: 100K tokens/month
2023 · netmind.ai · its alternatives →
3
I built a game to help you learn neural network architectures▲321
2024 · graphgame.sabrina.dev · its alternatives →
4
I'm 15 and built a neural network from scratch in C++,just math▲8
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 · its alternatives →
5
Neural networks tutorial series with code▲107
The simplest form of an artificial neural network explained and demonstrated. - GitHub - gokadin/ai-simplest-network: The simplest form of an artificial neural network explained and demonstrated.
2020 · github.com · its alternatives →
6AV
A Visual and Interactive Guide to the Basics of Neural Networks▲63
Discussions: Hacker News (63 points, 8 comments), Reddit r/programming (312 points, 37 comments) Translations: Arabic, French, Spanish Update: Part 2 is now live: A Visual And Interactive Look at Basic Neural Network Math Motivation I’m not a machine learning expert. I’m a software engineer by train
2016 · jalammar.github.io · its alternatives →
7N5
NN-512 – Generate standalone C code for neural nets▲111
2020 · nn-512.com · its alternatives →
8
Deep Learning Framework from scratch (60 step tutorial)▲40
Want to create a Deep Learning framework from scratch? Checkout this resource: https://koki0702.github.io/dezero-book/en/index.html In this book, you will create a deep learning framework called "DeZero" from scratch (from zero), which is the original framework of this book. With minimal code, the framework's modern features are realized. In this book, you will make this small - yet powerful enough - framework in a total of 60 steps. It will deepen your knowledge of modern frameworks such as PyTorch and TensorFlow.
2022 · koki0702.github.io · its alternatives →
9
To better understand Neural Networks I built one from scratch in C++▲10
Trying to understand neural nets by building a simple one from scratch - kretash/BasicNeuralNet
2017 · github.com · its alternatives →
10
Simple and comprehensive tutorials in Deep Learning using TensorFlow▲157
Simple and comprehensive tutorials in TensorFlow. Contribute to easy-tensorflow/easy-tensorflow development by creating an account on GitHub.
2018 · github.com · its alternatives →
11
Neural Network Evolution Playground with Backprop NEAT▲73
2016 · blog.otoro.net · its alternatives →
12
TorchGAN – Research Framework for Modelling GANs Based on Pytorch▲56
Research Framework for easy and efficient training of GANs based on Pytorch - torchgan/torchgan
2018 · github.com · its alternatives →
13
Single neuron neural network written in COBOL▲94
A perceptron written in COBOL. Contribute to victorqribeiro/perceptronCobol development by creating an account on GitHub.
2020 · github.com · its alternatives →
14IC
I created a Neural Network from scratch, in scratch▲7
I spent the past week implementing a 1 Layer Neural Net and training it on MNIST within the visual scripting language provided by scratch.mit.edu. It was tedious, but ultimately not too difficult. The code runs incredibly slowly, so much so that 64 samples of MNIST takes 5+ hours to train on my machine. There were a lot of little mini challenges that were fun to overcome (implementing softmax was very tricky). If you're interested, I encourage you to try and improve on it! More details in the linked blog post.
2024 · bell-boy.github.io · its alternatives →
15
A competition for small neural networks that play strategy gamesfree▲116
A ranked ladder for small neural networks that play strategy games. Train a model, write an adapter, upload two files, and get measured into a weight class from 8 KiB up.
Sep 2026 · tinybrains.dev · its alternatives →
16IM
I made a 5 hour neural networks class with no prerequisites▲17
2017 · neuralnetsforhackers.teachable.com · its alternatives →
17
Deep Learning for Program Synthesis▲93
By Rishabh Singh, Jacob Devlin, Abdelrahman Mohamed, and Pushmeet Kohli, Microsoft Research Despite the many advances in computing over the past decades, the actual process of writing computer software has not fundamentally changed — a programmer must manually code the exact algorithmic logic of a p
2017 · microsoft.com · its alternatives →
18
A Implementation of Alpha Zero for Chess in MLX▲71
A chess engine implementation inspired by AlphaZero, using MLX for neural network computations and Monte Carlo Tree Search (MCTS) for move selection.
2025 · github.com · its alternatives →
19
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 →
20
PyTorch Neural Network Guide▲2
Build your first neural network with PyTorch step by step.
Aug 2026 · khayyamshah2007.blogspot.com · its alternatives →
21
PyTorch Beginner's Guide▲4
Learn PyTorch from scratch with simple examples.
Aug 2026 · khayyamshah2007.blogspot.com · its alternatives →
22
Machine Translation Course from Scratch Using Deep Learning▲18
:speech_balloon: Sequence to Sequence from Scratch Using Pytorch - astorfi/sequence-to-sequence-from-scratch
2019 · github.com · its alternatives →
23
I wrote a minimal deep learning library from scratch in Python▲25
A minimalist deep learning library written from scratch in Python - sradc/SmallPebble
2021 · github.com · its alternatives →
24
Self-taught neural network learns to shoot a target▲60
Watch a Neural Network learns to shoot a target. Contribute to victorqribeiro/bangBangML development by creating an account on GitHub.
2020 · 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 →