Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Summarizing text

Reading comprehension (RC) is the to read text, process it, and its meaning. There are two types of summarization: extractive and abstractive. Extractive summarization identifies text and throws away the rest, leaving the passage shorter. Depending on the implementation, it can sound weird and disjointed since text is plucked from different paragraphs. Abstractive summarization is a lot more and it requires the model to understand the text and language in more depth. In the following recipe, we will implement a text summarization algorithm with the TensorFlow framework.

How to do it...

  1. We start by loading all the libraries, as follows:
 import numpy as np
 import tensorflow as tf
  1. First, we load the text data:
article_filename = 'Data/summary/"Data/sumdata/train/train.article.txt'
title_filename = 'Data/summary/"Data/sumdata/train/train.title.txt'

with open(article_filename) as article_file:
 articles = article_file.readlines()
with open(title_filename) as title_file: