To get the most out of this book

1. It would be great if you have basic proficiency in ML and Python.
2. An avid interest in data analysis, ML, and deep learning would be beneficial.

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/PacktPublishing/Hands-On-Transfer-Learning-with-Python. If 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: https://www.packtpub.com/sites/default/files/downloads/HandsOnTransferLearningwithPython_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: "Once complete, remember to add the following lines to the end of ~/.bashrc using your favorite editor (we used vim)."

A block of code is set as follows:

import glob 
import numpy as np 
import os 
import shutil 
from utils import log_progress 
 
np.random.seed(42) 

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

preprocessor = Preprocess()
corpus_to_seq = preprocessor.fit(corpus=corpus)

holdout_corpus = test_df['review'].values
holdout_target = test_df['sentiment'].values
holdout_corpus_to_seq = preprocessor.transform(holdout_corpus)

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

ubuntu@ip:~$ mkdir ssl
ubuntu@ip:~$ cd ssl
ubuntu@ip:~/ssl$

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: "Once you launch your instance, you can check out the Instances section and try to connect to the instance."

Warnings or important notes appear like this.

Tips and tricks appear like this.