AI · alternatives · 2026

24 alternatives to To better understand Neural Networks I built one from scratch in C++

Trying to understand neural nets by building a simple one from scratch - kretash/BasicNeuralNet

This is a C++ neural network implementation built from scratch as an educational project. It is designed for developers and students who want to understand how neural networks work at a fundamental level by examining… 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. To better understand Neural Networks I built one from scratch in C++ launched in 2017; newer entries below may have overtaken it.

1
I built a game to help you learn neural network architectures▲321
2024 · graphgame.sabrina.dev · its alternatives →
2
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 →
3AV
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 →
4
Simple Neural Network▲7
Simple way to learn neural network on C++
2023 · github.com · its alternatives →
5
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 →
6N5
NN-512 – Generate standalone C code for neural nets▲111
2020 · nn-512.com · its alternatives →
7
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 →
8
Applying the Unix philosophy to neural networks▲251
Neural network inference the Unix way. Contribute to cloudkj/layer development by creating an account on GitHub.
2019 · github.com · its alternatives →
9
Collection of deep learning implementations with side-by-side notes▲251
2021 · nn.labml.ai · its alternatives →
10
Perceptron – Artificial Neural Network Builder▲61
A flexible artificial neural network builder to analyse performance, and optimise the best model. - GitHub - casparwylie/Perceptron: A flexible artificial neural network builder to analyse performance, and optimise the best model.
2017 · github.com · its alternatives →
11
Comprehensive Tutorials in Deep Learning Using TensorFlow▲327
:satellite: Simple and ready-to-use tutorials for TensorFlow - GitHub - instillai/TensorFlow-Course: :satellite: Simple and ready-to-use tutorials for TensorFlow
2018 · github.com · its alternatives →
12
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 →
13
I trained a neural network to write Kanji▲166
sketch-rnn model trained to write kanji.
2018 · otoro.net · its alternatives →
14IM
I made a 5 hour neural networks class with no prerequisites▲17
2017 · neuralnetsforhackers.teachable.com · its alternatives →
15
BlockDL▲12
Hi everyone. Designing neural network architectures is inherently a visual process. Every time I train a new model, I find myself sketching it out on paper before translating it into code (and still running into shape mismatches no matter how many networks I've built). So I built BlockDL: a free and open-source tool to design neural networks visually in your browser. It updates live Python/Keras code as you build, and automatically checks for shape and connectivity errors in real time. It supports: - Most common layer types (Conv2D, Dense, LSTM, etc.) - Multiple input/output…
2025 · blockdl.com · its alternatives →
16
Reimplementing PyTorch from scratch (MLP, CNN) to learn the internals▲5
Implement classic neural networks from scratch without using nn.Module, to deeply understand PyTorch and model principles. - geyuxu/nn-from-scratch
Feb 2026 · github.com · its alternatives →
17
Comgra: A library for debugging and understanding neural networks▲66
I'm a machine learning engineer and researcher. I got fed up with how difficult it is to understand why neural networks behave the way they do, so i wrote a library to help with it. Comgra (computation graph analysis) is a library you can use with pytorch to extract all the tensor data you care about and visualize it graphically in a browser. This allows for a much more detailed analysis of what is happening than the usual approach of using tensorboard. You can go investigate tensors as training proceeds, drill down into individual neurons, inspect single data sets that are of special…
2023 · github.com · its alternatives →
18IA
Inside a Neural Network▲13
An introduction to building a basic feedforward neural network with backpropagation in Python.
2018 · enlight.nyc · its alternatives →
19
I built a small browser engine from scratch in C++▲146
Hi HN! Korean high school senior here, about to start CS in college. I built a browser engine from scratch in C++ to understand how browsers work. First time using C++, 8 weeks of development, lots of debugging—but it works! Features: - HTML parsing with error correction - CSS cascade and inheritance - Block/inline layout engine - Async image loading + caching - Link navigation + history Hardest parts: - String parsing(html, css) - Rendering - Image Caching & Layout Reflowing What I learned (beyond code): - Systematic debugging is crucial - Ship with known bugs rather than chase…
Jan 2026 · github.com · its alternatives →
20IC
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 →
21
Recurrent neural net plays 'neural slime volleyball' in JavaScript▲147
2015 · otoro.net · its alternatives →
22
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 →
23
I trained a neural network to learn Arabic morphology▲183
Arabic root finder with neural nets! Contribute to tb0yd/rootfinder development by creating an account on GitHub.
2018 · github.com · its alternatives →
24
Sonnet▲134
A new library for constructing neural networks from DeepMind
2017 · deepmind.google · its alternatives →

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