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Products that do what I'm 15 and built a neural network from scratch in C++,just math does
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…
- 1IB
2024 · graphgame.sabrina.dev
- 2IB
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
- 3TB
2017 · github.com
- 4IM
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
- 5IW
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
- 6
- 7IB
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
- 8FD
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
- 9IC
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
- 10MA
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
- 11NN
2020 · github.com
- 12AI
2023 · x0axz.com
- 13IC
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
- 14NN
Hi HN. Peter here. As a machine learning engineer, I mostly think in terms of feature vectors, embeddings, and matrices. One of the most useful byproducts of deep neural networks is embeddings because they allow us to represent high-dimensional data in terms of lower-dimensional latent vectors. These feature vectors can be used for downstream applications like similarly search, recommendation systems and near duplicate detection. As an ML engineer, I was frustrated by the lack of a datastore in which vectors are first-class citizens. As a result, most ML engineers, including myself, end up…
2021
- 15MC
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
- 16CM
I'm a machine learning engineer who always found it annoying to integrate ML models into phone apps, smartwatch apps, microcontroller firmware etc... Why do we need all these libraries and runtimes with all the overhead, compatibility issues and other headaches, when it's just some math to be executed? So I made a compiler that simply converts the model into plain source code with no dependencies, and it actually solved all my deployment problems. Now I'm curious if it can help anyone else too. Through the link you can submit your model file (Keras h5, onnx soon to be supported), and I'll…
2023 · waveworks.dk
- 17AC
built a tiny pytorch clone in c after going through prof. vijay janapa reddi's mlsys book: mlsysbook.ai/tinytorch/ perfect for learning how ml frameworks work under the hood :)
Dec 2025 · github.com
- 18IW
2021 · github.com
- 19IW
2024 · gitlab.com
- 20IB
The HN community may find the context of the prompts, organized by each turn in each session, the most useful. See the website/docs/prompts.md and session-X.md files. I also started exploring some workflows for the LLM to execute, organized in the website/docs/tasks/ folder. I found it pretty handy to have the LLM document our work as we went and simply embedded the static site into the executable, along with all the music and logic. The whole project took me about a day for the backend. The C++ controller itself took only a few turns. I enjoyed focusing on my son's…
2025 · github.com
- 21DL
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
- 22W1
Hey HackerNews, I built this project over the last few weeks as a palette cleanser from a failed game launch. I wanted to learn a bit about AI/Neural-Networks and naively thought I could build a tiny maze-solving AI in a weekend with a 100% solve rate. Well - I couldn't, but I got pretty close. 14 Bytes total model size, and a 96.5% solve rate on unseen mazes. Trained across 46 phases experimenting with different ideas to improve the model (better performance, smaller size). Its quite fun to watch the model attempt to solve the maze, when they fail its usually due to getting stuck in a…
Jul 2026 · con-dog.github.io
- 23IB
Hey all, I’ve been working on a project that aims to change how we approach learning new skills. The idea is simple — you can master any skill in 30 days using a personalized learning plan powered by AI and Notion. They keys: practice and discipline. How it works: You can fill out a Typeform, and our AI will create a customized 30-day program just for you. Structured learning: Each day, the system provides you with a clear task or lesson based on science-backed learning principles. Track progress: It’s all managed in Notion, so you can stay organized and motivated while learning. Why I built…
2025 · 30daysmethod.com
- 24IT
From 2020-23 I taught at a highly accelerated math program in Pasadena, CA and was given the opportunity to develop and teach a quantitative computer science track for participating 10th-12th graders who had an interest in CS. I developed the curriculum on the fly while teaching the courses, and eventually consolidated it all into this textbook. The students had already learned a surprising amount of math for their age using a system that is now commercially available at www.mathacademy.com, where they progressed from prealgebra through AP Calculus BC in only three years (6th-8th grade), and…
2023 · justinmath.com
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