This module explains how to build up a basic neural network step-by-step. It also uses the MNIST dataset for detecting numbers from handwriting.

Neural Networks: Beginners to Advanced

Machine learning is one of the fastest growing fields. This module aims to teach one of the fundamental concepts of machine learning, that is neural networks. You will learn the basic concepts of building a model as well as the mathematical explanation behind the neural network. You will learn to build the neural network from scratch, in Python. You will also learn how to train and optimize your network to achieve a better result. We’ve specifically designed this module for beginners so it doesn’t require any prior programming experience. Happy learning!

Make Your Own Neural Network in Python

## Introduction 

For thousands of years, we humans have tried to understand how our intelligence works, and we've tried to replicate it with machines—thinking machines. 

We have not been satisfied by creating mechanical or electronic machines to help us with simple tasks, like using flint to spark fires, lifting heavy rocks with pulleys, or using calculators for arithmetic. 

Instead, we want to automate more challenging and complex tasks like grouping similar photos, distinguishing diseased cells from healthy ones, or even playing a decent game of chess. These tasks seem to require human intelligence, or at least a deeper capability of the human mind, which is not found in simple machines like calculators.

Machines with human-like intelligence is such a powerful idea that our culture
is full of fantasies and fears about—the immensely capable but ultimately menacing HAL 9000 in Stanley Kubrick's *2001: A Space Odyssey*, the robots in *Terminator*, and the
talking KITT car with a cool personality from the classic *Knight Rider* TV series.

When Gary Kasparov, the world chess champion (1985–2000) 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.

Our desire for intelligent machines is so strong that some have fallen for the temptation to cheat. The infamous mechanical Turk chess machine was merely a hidden person inside a cabinet!

# Introduction 

For thousands of years, we humans have tried to understand how our intelligence works, and we've tried to replicate it with machines—thinking machines. 

We have not been satisfied by creating mechanical or electronic machines to help us with simple tasks, like using flint to spark fires, lifting heavy rocks with pulleys, or using calculators for arithmetic. 

Instead, we want to automate more challenging and complex tasks like grouping similar photos, distinguishing diseased cells from healthy ones, or even playing a decent game of chess. These tasks seem to require human intelligence, or at least a deeper capability of the human mind, which is not found in simple machines like calculators.

Machines with human-like intelligence is such a powerful idea that our culture
is full of fantasies and fears about—the immensely capable but ultimately menacing HAL 9000 in Stanley Kubrick's *2001: A Space Odyssey*, the robots in *Terminator*, and the
talking KITT car with a cool personality from the classic *Knight Rider* TV series.

When Gary Kasparov, the world chess champion (1985–2000) 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.

Our desire for intelligent machines is so strong that some have fallen for the temptation to cheat. The infamous mechanical Turk chess machine was merely a hidden person inside a cabinet!

Learn about intelligent machines and the potential they hold for the tech industry in the future. 

Prologue

Part 1 - A Little Background

Part 2 - Let's Get Started!

Part 3 - Backward Propagation of Error

Part 4 - Adjusting the Link Weights

Part 5 - A Gentle Start with Python

Part 6 - Neural Network with Python

Part 7 - Testing Neural Network against MNIST Dataset

Part 8 - Some Suggested Improvements

Part 9 - Even More Fun!

Appendix: A Small Guide to Calculus

Conclusion

The Search for Intelligent Machines

Introduction