AI · alternatives · 2026

24 alternatives to I built 10 ML algos from scratch because fit() predict() are not enough

Companion code for Machine Learning From Scratch — 10 core ML algorithms built from scratch with NumPy, compared with Scikit-learn and PyTorch. - ml-from-scratch-book/code

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

1
ML From Scratch – free online textbook▲259
2020 · dafriedman97.github.io · its alternatives →
2
MakeML▲274
Train Neural Networks without a line of code
2019 · makeml.app · its alternatives →
3
TensorFlow 1.0▲823
Open-source machine learning library by Google
2017 · tensorflow.org · its alternatives →
4
ML5.js▲249
Friendly machine learning for the web
2018 · ml5js.org · its alternatives →
5
ML Kit (by Google)▲195
Machine learning for mobile developers
2018 · developers.google.com · its alternatives →
6
Alchemy by Fritz▲90
The easiest way to convert a neural network to Core ML
2018 · its alternatives →
7
I built a tensor library from scratch in C++/CUDA▲119
Hi HN, Over the past few months, I've been building `dsc`, a tensor library from scratch in C++/CUDA. My main focus has been on getting the basics right, prioritizing a clean API, simplicity, and clear observability for running small LLMs locally. The key features are: - C++ core with CUDA support written from scratch. - A familiar, PyTorch-like Python API. - Runs real models: it's complete enough to load a model like Qwen from HuggingFace and run inference on both CUDA and CPU with a single line change[1]. - Simple, built-in observability for both Python and C++. Next on the roadmap is…
2025 · github.com · its alternatives →
8
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 →
9
Compile ML models into dependency-free source code for easy deployment▲37
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 · its alternatives →
10
Not Hotdog Tutorial 🌭▲103
Guide to build a machine learning app in 30 min without code
2018 · airdev.co · its alternatives →
11CR
Carton – Run any ML model from any programming language▲196
The goal of Carton is to let you use a single interface to run any machine learning model from any programming language. It’s currently difficult to integrate models that use different technologies (e.g. TensorRT, Ludwig, TorchScript, JAX, GGML, etc) into your application, especially if you’re not using Python. Even if you learn the details of integrating each of these frameworks, running multiple frameworks in one process can cause hard-to-debug crashes. Ideally, the ML framework a model was developed in should just be an implementation detail. Carton lets you decouple your application from…
2023 · carton.run · its alternatives →
12
GUIs for Faster ML Prototyping and Sharing▲85
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work! - gradio-app/gradio
2020 · github.com · its alternatives →
13
Fullstack ML – From Notebooks to Deployment▲198
Contribute to xadrianzetx/fullstack.ai development by creating an account on GitHub.
2019 · github.com · its alternatives →
14
Nyckel▲119
The machine learning API for non-experts
2022 · nyckel.com · its alternatives →
15ML
Machine learning cheat sheet▲166
2011 · eferm.com · its alternatives →
16
Build Machine Learning Web-Service with Python and Django▲67
My Machine Learning Web Service. Contribute to pplonski/my_ml_service development by creating an account on GitHub.
2020 · github.com · its alternatives →
17
Autograd.c – A tiny ML framework built from scratch▲85
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 · its alternatives →
18
Local LLMs by Sttabot AI▲112
Build local LLMs using top data science libraries
2023 · its alternatives →
19
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 →
20
PyBroker – Algotrading in Python with Machine Learning▲70
Hello, I am excited to share PyBroker with you, a free and open-source Python framework that I developed for creating algorithmic trading strategies, including those that utilize machine learning. With PyBroker, you can easily develop and fine-tune trading rules, build powerful ML models, and gain valuable insights into your strategy's performance. Some of the key features of PyBroker include: - A super-fast backtesting engine built using NumPy and accelerated with Numba. - The ability to create and execute trading rules and models across multiple instruments with ease. - Access to…
2023 · github.com · its alternatives →
21
Machine learning (entirely) in Rust▲20
Machine Learning library for Rust. Contribute to AtheMathmo/rusty-machine development by creating an account on GitHub.
2016 · github.com · its alternatives →
22
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 →
23BP
Build predictive models with no prior ML experience▲70
TLDR: Product developers need tools designed for them to build ML models. We’d love for you to try a demo of Mage without needing to sign up: https://www.mage.ai/onboarding My name is Tommy DANGerous (or Tommy Dang) and I’m the CEO and co-founder at Mage. I worked at Airbnb for over 5 years as a product developer building features for guests. Mage is a web-based tool for building, training, and deploying ML models that make predictions based off your data. Training and using ML models in production typically requires working knowledge of building data pipelines, algorithms,…
2022 · its alternatives →
24HP
High-performance ahead-of-time compiler for Machine Learning▲83
2018 · scss.tcd.ie · its alternatives →

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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 →