I wrote my first neural network
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.
I wrote my first neural network launched on March 14, 2026 with 6 votes, #1,631 of 23,380 launches that month and more than 75% of that year's launches.
What it does
A simple Perceptron implementation. Contribute to stupid-genius/Perceptron development by creating an account on GitHub.
A full, multi-layer perceptron implemented in JavaScript. Uses dual numbers for auto-diff to perform backpropagation. Inspired by the excellent video series by Welch Labs on YouTube . const { Perceptron } = require ( './Perceptron.js' ) ; // 1. Initialize: 2 inputs, 3 hidden neurons, 1 output const p = new Perceptron ( [ 2 , 3 , 1 ] ) ; // 2. Configure layers (optional) // You can set activation globally: p . activation = Perceptron . RELU ; // Or fine-tune per-layer using the Proxy: p [ 1 ] . activation = Perceptron . SIGMOID ; // 3. Forward pass const output = p . forward ( 0.5 , - 0.2 ) ; console . log ( `Prediction: ${ output [ 0 ] [ 0 ] . real } ` ) ; // 4. Training step p . backward (…from github.com
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Comprehensive Tutorials in Deep Learning Using TensorFlow2018 · github.com · ▲327:satellite: Simple and ready-to-use tutorials for TensorFlow - GitHub - instillai/TensorFlow-Course: :satellite: Simple and ready-to-use tutorials for TensorFlow
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The book page links to a blog post that explains how I got about it (and has a link to sample content), but the TL&DR is that I could not find a lot of books that were on "our" history _and_ were larded with technical details. So I set about writing one, and some five years later I'm happy to share the result. I think it's one of the few "computer history" books that has tons of code, but correct me if I'm wrong (I wrote this both to tell a story and to learn :-)). My favorite languages are Smalltalk and Lisp, but as an Emacs user, I've been using the latter for much longer and for my…
- AVA Visual and Interactive Guide to the Basics of Neural Networks2016 · jalammar.github.io · ▲63
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I'm 15 and built a neural network from scratch in C++,just math2025 · github.com · ▲8I’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…
- ICI created a Neural Network from scratch, in scratch2024 · bell-boy.github.io · ▲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.
I wrote my first Neural Network2017 · github.com · ▲6
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