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

## Sigmoid function in `scipy`

Here's the formula of the sigmoid function:

$$

O_\text{hidden} = \text{sigmoid}( X_\text{hidden} )

$$

This should be easy, especially if the sigmoid function is already defined in a Python library. It turns out that it is. The `scipy` library has a set of special functions, and the sigmoid function is called `expit()`. This library is imported, just like we imported the `numpy` library:


# Sigmoid function in `scipy`

Here's the formula of the sigmoid function:

$$

O_\text{hidden} = \text{sigmoid}( X_\text{hidden} )

$$

This should be easy, especially if the sigmoid function is already defined in a Python library. It turns out that it is. The `scipy` library has a set of special functions, and the sigmoid function is called `expit()`. This library is imported, just like we imported the `numpy` library:


Learn how to use the sigmoid function to calculate the outputs of the hidden and final layers.

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

Apply the Sigmoid Function

Sigmoid function in `scipy`