To get the most out of this book

This book will be most applicable to professionals and students interested in data analysis and want to enhance their knowledge in the field of developing applications using TensorFlow and Keras. For the best experience, you should have knowledge of programming fundamentals and some experience with Python. In particular, having some familiarity with Python libraries such as Pandas, matplotlib, and scikit-learn will be useful.

Download the example code files

You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.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.packtpub.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/TrainingByPackt/Applied-Deep-Learning-with-Python. 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/TrainingByPackt/Applied-Deep-Learning-with-Python. Check them out!

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: "We can see the NotebookApp being run on a local server."

A block of code is set as follows:

fig, ax = plt.subplots(1, 2)
sns.regplot('RM', 'MEDV', df, ax=ax[0],
scatter_kws={'alpha': 0.4}))
sns.regplot('LSTAT', 'MEDV', df, ax=ax[1],
scatter_kws={'alpha': 0.4}))

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

    cat chapter-1/requirements.txt
    matplotlib==2.0.2
    numpy==1.13.1
    pandas==0.20.3
    requests==2.18.4

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

pip install version_information 
pip install ipython-sql

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Notice how the white dress price was used to pad the missing values."

Warnings or important notes appear like this.

Tips and tricks appear like this.