This book will give you an overview of data analysis in Python. This will take you through the main libraries of Python's data science stack. It will explain how to use various Python tools to analyze, visualize, and process data effectively and you will learn about the importance of using GPUs in deep learning. The reader must have software and hardware experience in Python development.

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

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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Generative-Adversarial-Networks-with-Keras. 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!

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/9781789538205_ColorImages.pdf.

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: "We are going to update all Anaconda packages using the conda command."

A block of code is set as follows:

import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.utils import np_utils
from keras.optimizers import SGD
from keras.layers.core import Dense,Activation

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

pip install keras