#### Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Python Machine Learning Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
The Realm of Supervised Learning
Visualizing Data
Index

## Building a deep neural network

We are now ready to build a deep neural network. A deep neural network consists of an input layer, many hidden layers, and an output layer. This looks like the following:

The preceding figure depicts a multilayer neural network with one input layer, one hidden layer, and one output layer. In a deep neural network, there are many hidden layers between the input and the output layers.

### How to do it…

1. Create a new Python file, and import the following packages:

```import neurolab as nl
import numpy as np
import matplotlib.pyplot as plt```
2. Let's define parameters to generate some training data:

```# Generate training data
min_value = -12
max_value = 12
num_datapoints = 90```
3. This training data will consist of a function that we define that will transform the values. We expect the neural network to learn this on its own, based on the input and output values that we provide:

```x = np.linspace(min_value, max_value, num_datapoints)
y = 2 * np.square(x) + 7
y /= np.linalg.norm(y)```
4. Reshape the...