Make Your Own Neural Network

Jul 6, 2026

Upload by Noah

Make Your Own Neural Network guides readers through the principles and practice of neural networks, starting from biologically inspired ideas and moving to concrete mechanisms like prediction, classification, and matrix multiplication. The book explains how training works by learning weights from data and updating them via backpropagating errors, including worked examples. It also shows how to implement and experiment with neural networks using Python and even a Raspberry Pi, training on handwritten digit data (MNIST) and generating new training inputs through rotations.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages224

Reading Time9 h 24 min

Prologue

The Search for Intelligent Machines

A Nature Inspired New Golden Age

Introduction

Who is this book for?

What will we do?

How will we do it?

Part 1—How They Work

A Simple Predicting Machine

A Simple Classifier

Neurons, Nature’s Computing Machines

Learning Weights From More Than One Node

Backpropagating Errors

How Do We Actually Update Weights?

Weight Update Worked Example

Preparing Data

Part 2—DIY with Python

Interactive Python = IPython

A Very Gentle Start with Python

Neural Network with Python

The MNIST Dataset of Handwritten Numbers

Part 3—Even More Fun

Your Own Handwriting

Creating New Training Data: Rotations

Appendix A

A Gentle Introduction to Calculus

Appendix B

Do It with a Raspberry Pi

Contents Prologue The Search for Intelligent Machines A Nature Inspired New Golden Age Introduction Who is this book for? What will we do? How will we do it? Author’s Note Part 1-How They Work Easy for Me, Hard for You A Simple Predicting Machine Classifying is Not Very Different from Predicting Training A Simple Classifier Sometimes One Classifier Is Not Enough Neurons, Nature’s Computing Machines Following Signals Through A Neural Network Matrix Multiplication is Useful .. Honest! A Three Layer Example with Matrix Multiplication Learning Weights From More Than One Node Backpropagating Errors From More Output Nodes Backpropagating Errors To More Layers Backpropagating Errors with Matrix Multiplication How Do We Actually Update Weights? Weight Update Worked Example Preparing Data Part 2-DIY with Python Python Interactive Python = IPython A Very Gentle Start with Python Neural Network with Python The MNIST Dataset of Handwritten Numbers Part 3-Even More Fun Your Own Handwriting Inside the Mind of a Neural Network Creating New Training Data: Rotations Epilogue Appendix A: A Gentle Introduction to Calculus A Flat Line A Sloped Straight Line A Curved Line Calculus By Hand Calculus Not By Hand Calculus without Plotting Graphs Patterns Functions of Functions You can do Calculus! Appendix B: Do It with a Raspberry Pi Installing IPython Making Sure Things Work Training And Testing A Neural Network Raspberry Pi Success! Prologue The Search for Intelligent Machines For thousands of years, we humans have tried to understand how our own intelligence works and replicate it in some kind of machine-thinking machines. We’ve not been satisfied by mechanical or electronic machines helping us with simple tasksflint sparking fires, pulleys lifting heavy rocks, and calculators doing arithmetic. Instead, we want to automate more challenging and complex tasks like grouping similar photos, recognising diseased cells from healthy ones, and even putting up a decent game of chess. These tasks seem to require human intelligence, or at least a more mysterious deeper capability of the human mind not found in simple machines like calculators. Machines with this human-like intelligence is such a seductive and powerful idea that our culture is full of fantasies, and fears, about it -the immensely capable but ultimately menacing HAL 9000 in Stanley Kubrick’s 2001: A Space Odyssey, the crazed action Terminator robots and the talking KITT car with a cool personality from the classic Knight Rider TV series. When Gary Kasparov, the reigning world chess champion and grandmaster, was beaten by the IBM Deep Blue computer in 1997 we feared the potential of machine intelligence just as much as we celebrated that historic achievement. So strong is our desire for intelligent machines that some have fallen for the temptation to cheat. The infamous mechanical Turk chess machine was merely a hidden person inside a cabinet! A Nature Inspired New Golden Age Optimism and ambition for artificial intelligence were flying high when the subject was formalised in the 1950s. Initial successes saw computers playing simple games and proving theorems. Some were convinced machines with human level intelligence would appear within a decade or so. But artificial intelligence proved hard, and progress stalled. The 1970s saw a devastating academic challenge to the ambitions for artificial intelligence, followed by funding cuts and a loss of interest. It seemed machines of cold hard logic, of absolute 1s and 0s, would never be able to achieve the nuanced organic, sometimes fuzzy, thought processes of biological brains. After a period of not much progress an incredibly powerful idea emerged to lift the search for machine intelligence out of its rut. Why not try to build artificial brains by copying how real biological brains worked? Real brains with neurons instead of logic gates, softer more organic reasoning

FAQs

Who is this book intended for?

The book is written for anyone who wants to understand what neural networks are, make and use their own, and appreciate the core mathematical ideas without needing expert-level math or computer science.

What problem does the book focus on for building a neural network?

It focuses on training a neural network to recognize handwritten characters, including using the MNIST dataset and learning the corresponding classification behavior.

How does training work in the neural network described in the book?

Training learns weights from data and improves predictions by backpropagating errors, then updating weights through a matrix-based weight update process demonstrated with worked examples.

Jul 6, 2026

Upload by Noah