Book Image

Natural Language Processing with TensorFlow

By : Motaz Saad, Thushan Ganegedara
Book Image

Natural Language Processing with TensorFlow

By: Motaz Saad, Thushan Ganegedara

Overview of this book

Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks. Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator. After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.
Table of Contents (16 chapters)
Natural Language Processing with TensorFlow

Our data

First, we will discuss the data we will use for text generation and various preprocessing steps employed to clean data.

About the dataset

First, we will understand what the dataset looks like so that when we see the generated text, we can assess whether it makes sense, given the training data. We will download the first 100 books from the website These are translations of a set of books (from German to English) by the Brothers Grimm. This is the same as the text used in Chapter 6, Recurrent Neural Networks, for demonstrating the performance of RNNs.

Initially, we will download the first 100 books from the website with an automated script, as follows:

url = ''

# Create a directory if needed
dir_name = 'stories'
if not os.path.exists(dir_name):
def maybe_download(filename):
  """Download a file if not present"""
  print('Downloading file: ', dir_name+ os.sep+filename)
  if not...