AI · alternatives · 2026
24 alternatives to I'm 15 and built a neural network from scratch in C++,just math
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…
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.
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2024 · graphgame.sabrina.dev · its alternatives →
- 2AV
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 →
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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 →
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Trying to understand neural nets by building a simple one from scratch - kretash/BasicNeuralNet
2017 · github.com · its alternatives →
- 5HI
I have been taking Andrew Ng's Machine Learning course. The lecture module on Neural Networks ends with an intriguing video of the ALVINN autonomous car driving itself along normal roads at CMU in the mid 90s. I was inspired by that video to see what I could build myself. A link to my incomplete write up was posted a couple of weeks ago (ouch!) but I finally finished it and open sourced the code tonight (so you can build your own self driving car). What do you think? http://blog.davidsingleton.org/nnrccar https://github.com/dps/nnrccar
2012 · blog.davidsingleton.org · its alternatives →
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Hey! I’m JC. I’m 17 and part of Hack Club, a nonprofit where we help teenagers ship programming projects with their friends while growing technically. A while ago, I asked myself the question, “How exactly do programming languages work behind the scenes?” It seemed really daunting until I went to a half hour workshop at a high school hackathon about writing a tree-walk interpreter and realized that getting started was actually super fun. This guide is designed in the vein of that - to get people, especially teenagers, started on learning how to build a programming language in a literal…
2024 · easel.hackclub.com · its alternatives →
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: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 →
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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 · its alternatives →
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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 →
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Simple and comprehensive tutorials in TensorFlow. Contribute to easy-tensorflow/easy-tensorflow development by creating an account on GitHub.
2018 · github.com · its alternatives →
- 12IB
We wanted to do something very challenging to prove to ourselves that we can do anything we put our mind to. The reasoning for why we chose to build a toy TPU specifically is fairly simple: - Building a chip for ML workloads seemed cool - There was no well-documented open source repo for an ML accelerator that performed both inference and training None of us have real professional experience in hardware design, which, in a way, made the TPU even more appealing since we weren't able to estimate exactly how difficult it would be. As we worked on the initial stages of this project, we…
2025 · tinytpu.com · its alternatives →
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I worked on this applied Deep Reinforcement Learning course for the better part of 2021. I made a Datacamp course [0] before, and this served as my inspiration to make an applied Deep RL series. Normally, Deep RL courses teach a lot of mathematically involved theory. You get the practical applications near the end (if at all). I have tried to turn that on its head. In the top-down approach, you learn practical skills first, then go deeper later. This is much more fun. This course (the first in a planned multi-part series) shows how to use the Deep Reinforcement Learning framework RLlib to…
2022 · courses.dibya.online · its alternatives →
- 14IC
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 →
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I made this tool to get some better intuition on how neural networks/backpropagation worked, but I'm really unsure what to do with it now, so I've put it up on github, and I wrote a little primer on backprop and neural networks to showcase it. Really curious to hear any thoughts you might have, or anything I got wrong in the write up!
2024 · github.com · its alternatives →
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A single / multi layer / recurrent neural network written in Golang. - made2591/go-perceptron-go
2017 · github.com · its alternatives →
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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 →
- 18IC
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 · its alternatives →
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A perceptron written in COBOL. Contribute to victorqribeiro/perceptronCobol development by creating an account on GitHub.
2020 · github.com · its alternatives →
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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 →
- 21AI
2023 · x0axz.com · its alternatives →
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A minimalist deep learning library written from scratch in Python - sradc/SmallPebble
2021 · github.com · its alternatives →
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Hey HN, I don’t know who else has the same issue, but: Textbooks often bury good ideas in dense notation, skip the intuition, assume you already know half the material, and get outdated in fast-moving fields like AI. Over the past 7 years of my AI/ML experience, I filled notebooks with intuition-first, real-world context, no hand-waving explanations of maths, computing and AI concepts. In 2024, a few friends used these notes to prep for interviews at DeepMind, OpenAI, Nvidia etc. They all got in and currently perform well in their roles. So I'm sharing. This is an open & unconventional…
Feb 2026 · github.com · its alternatives →
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Simple way to learn neural network on C++
2023 · github.com · its alternatives →
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