Life & fun · alternatives · 2026

24 alternatives to Make Your Own Neural Network

An In-depth Visual Introduction For Beginners

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Make Your Own Neural Network launched in 2017; newer entries below may have overtaken it.

1
Sonnet▲134
A new library for constructing neural networks from DeepMind
2017 · deepmind.google · its alternatives →
2
Machine Learning▲77
A visual guide for beginners
2017 · amazon.com · its alternatives →
3
Neural Networks and Deep Learning▲95
Core concepts behind neural networks and deep learning
2015 · neuralnetworksanddeeplearning.com · its alternatives →
4
The Math of Neural Networks▲5
A Visual Introduction For Beginners
2017 · amazon.com · its alternatives →
5
Deep Learning▲67
The textbook for modern machine learning
2016 · amazon.com · its alternatives →
6
Make Your Own Neural Network▲7
The Mathematics of Neural Networks
2017 · amazon.com · its alternatives →
7
I built a game to help you learn neural network architectures▲321
2024 · graphgame.sabrina.dev · its alternatives →
8
Quick Draw by Google▲197
Can a neural network learn to recognize doodles?
2016 · quickdraw.withgoogle.com · its alternatives →
9AV
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 →
10
Machine Learning for Absolute Beginners▲12
A Plain English Introduction to Machine Learning
2017 · amazon.com · its alternatives →
11
TensorFlow 1.0▲80
Open-source machine learning library by Google
2018 · tensorflow.org · its alternatives →
12
Deep Learning▲5
A book on neural nets for either industry or research
2017 · amazon.com · its alternatives →
13
Neural Network Generated Illustrations▲139
Create a personalized sticker pack in Allo
2017 · research.google · its alternatives →
14
An Introduction to Statistical Learning▲60
How to implement the various methods in real life settings
2016 · its alternatives →
15
Neural Painter▲85
Paint artistic patterns using random neural network
2016 · github.com · its alternatives →
16
WhatTheFIsML▲110
An illustrated guide to understanding Machine Learning
2016 · its alternatives →
17
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 →
18IM
I made a 5 hour neural networks class with no prerequisites▲17
2017 · neuralnetsforhackers.teachable.com · its alternatives →
19
Talking Machines - Solving intelligence and machine learning fundamentals▲56
Guest: Nando de Freitas (Oxford, Google DeepMind)
2015 · thetalkingmachines.com · its alternatives →
20
Neuron▲126
Teaching machines to learn
2016 · its alternatives →
21
Marching neural network▲8
Visualizing level surfaces of a neural network
2017 · arogozhnikov.github.io · its alternatives →
22
Neural Coref▲86
State-of-the-art coreference resolution based on neural nets
2017 · its alternatives →
23
Machine Learning▲1
A Visual Beginners Guide to Machine Learning with Python
2017 · amazon.com · its alternatives →
24GC
Graph Convolutional Networks – Intro to neural networks on graphs▲106
Many important real-world datasets come in the form of graphs or networks: social networks, knowledge graphs, protein-interaction networks, the World Wide Web, etc. (just to name a few). Yet, until recently, very little attention has been devoted to the generalization of neural...
2016 · tkipf.github.io · 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 →