To get the most out of this book

Readers will require the following hardware with an Intel Xenon CPU E5-1650 [email protected] GHz and 32 GB RAM with GPU support and Raspberry Pi 3. Additionally, some basic knowledge of Python and its libraries, such as pandas, NumPy, Keras, TensorFlow, scikit-learn, Matplotlib, Seaborn, OpenCV, and Beautiful Soup 4 will be helpful to grasp the concepts throughout the chapters.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you. You can download the code files by following these steps:

1. Log in or register at www.packt.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for Mac
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Deep-Learning-for-IoT. In case there's an update to the code, it will be updated on the existing GitHub repository. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781789616132_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book. CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "For the exploration, we can run image_explorer.py on the dataset as follows."

A block of code is set as follows:

# Import the required modules
import urllib
from bs4 import BeautifulSoup
from selenium import webdriver
import os, os.path
import simplejson

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.preprocessing import scale
from keras.models import Sequential

Any command-line input or output is written as follows:

$ mkdir css
$ cd css

Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Google Chrome | More tools | Developer tools (in Windows OS)."

Warnings or important notes appear like this.

Tips and tricks appear like this.