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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

## 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

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

Apply the Sigmoid Function

Sigmoid function in `scipy`