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Machine learning is one of the fastest growing fields, and we cannot emphasize enough about its importance. This course aims to teach one of the fundamental concepts of machine learning, i.e., Neural Network. You will learn the basic concepts of building a model as well as the mathematical explanation behind Neural Network and based on that; you will build one from scratch (in Python). You will also learn how to train and optimize your network to achieve a better result.

Make Your Own Neural Network in Python

## Backpropagation of errors

This method is used extensively when there is more than one node that contributes to the output. The following diagram shows a simple network with two input nodes, but with two output nodes.

# Backpropagation of errors

This method is used extensively when there is more than one node that contributes to the output. The following diagram shows a simple network with two input nodes, but with two output nodes.

Learn about error backpropagation from the output node into the network.

Prologue

A Little Background

Let's Get Started!

Backward Propagation of Error

Adjusting the Link Weights

A Gentle Start with Python

Neural Network with Python

Testing Neural Network against MNIST Dataset

Some Suggested Improvements

Even More Fun!

Epilogue

Appendix: A Small Guide to Calculus

Error Backpropagation from More Output Nodes

Backpropagation of errors