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Natural Language Processing with TensorFlow
Natural Language Processing with TensorFlow

Natural Language Processing with TensorFlow: Teach language to machines using Python's deep learning library

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Natural Language Processing with TensorFlow

Chapter 2. Understanding TensorFlow

In this chapter, you will get an in-depth understanding of TensorFlow. This is an open source distributed numerical computation framework, and it will be the main platform on which we will be implementing all our exercises.

We will get started with TensorFlow by defining a simple calculation and trying to compute it using TensorFlow. After we successfully complete this, we will investigate how TensorFlow executes this computation. This will help us to understand how the framework creates a computational graph to compute the outputs and execute this graph through something known as a session. Then we will gain a hands-on experience of the TensorFlow architecture by relating how TensorFlow executes things, with the help of an analogy of how a restaurant might operate.

Having gained a good conceptual and technical understanding of how TensorFlow operates, we will look at some of the important computational operations that the framework offers. First...

What is TensorFlow?

In Chapter 1, Introduction to Natural Language Processing, we briefly discussed what TensorFlow is. Now let's take a closer look at it. TensorFlow is an open source distributed numerical computation framework released by Google that is mainly intended to alleviate the painful details of implementing a neural network (for example, computing derivatives of the weights of the neural network). TensorFlow takes this even a step further by providing efficient implementations of such numerical computations using Compute Unified Device Architecture (CUDA), which is a parallel computational platform introduced by NVIDIA. The Application Programming Interface (API) of TensorFlow at https://www.tensorflow.org/api_docs/python/ shows that TensorFlow provides thousands of operations that make our lives easier.

TensorFlow was not developed overnight. This is a result of the persistence of talented, good-hearted individuals who wanted to make a difference by bringing deep learning...

Inputs, variables, outputs, and operations

Now with an understanding of the underlying architecture let's proceed to the most common elements that comprise a TensorFlow client. If you read any of the millions of TensorFlow clients available on the internet, they all (the TensorFlow-related code) fall into one of these buckets:

  • Inputs: Data used to train and test our algorithms
  • Variables: Mutable tensors, mostly defining the parameters of our algorithms
  • Outputs: Immutable tensors storing both terminal and intermediate outputs
  • Operations: Various transformations for inputs to produce the desired outputs

In our earlier example, in the sigmoid example, we can find instances of all these categories. We list the elements in Table 2.1:

TensorFlow element

Value from example client

Inputs

x

Variables

W and b

Outputs

h

Operations

tf.matmul(...), tf.nn.sigmoid(...)

The following subsections explain each of these TensorFlow elements in more detail.

Defining inputs in TensorFlow

The client...

Reusing variables with scoping

Until now, we have looked at the architecture of TensorFlow and the essentials required to implement a basic TensorFlow client. However, there is much more to TensorFlow than this. As we already saw, TensorFlow behaves quite differently from a typical Python script. For example, you cannot debug TensorFlow code in real time (as you would do a simple Python script using a Python IDE), as the computations do not happen in real time in TensorFlow (unless you are using the Eager Execution method, which was only recently in TensorFlow 1.7: https://research.googleblog.com/2017/10/eager-execution-imperative-define-by.html). In other words, TensorFlow first defines the full computational graph, does all computations on a device, and finally fetches results. Consequently, it can be quite tedious and painful to debug a TensorFlow client. This emphasizes the importance of attention to detail when implementing a TensorFlow client. Therefore, it is advised to adhere to...

Implementing our first neural network

Great! Now that you've learned the architecture, basics, and scoping mechanism of TensorFlow, it's high time that we move on and implement something moderately complex. Let's implement a neural network. Precisely, we will implement a fully connected neural network model that we discussed in Chapter 1, Introduction to Natural Language Processing.

One of the stepping stones to the introduction of neural networks is to implement a neural network that is able to classify digits. For this task, we will be using the famous MNIST dataset made available at http://yann.lecun.com/exdb/mnist/. You might feel a bit skeptical regarding our using a computer vision task rather than a NLP task. However, vision tasks can be implemented with less preprocessing and are easy to understand.

As this is our first encounter with neural networks, we will walk through the main parts of the example. However, note that I will only walk through the crucial bits of...

Summary

In this chapter, you took your first steps to solving NLP tasks by understanding the primary underlying platform (TensorFlow) on which we will be implementing our algorithms. First, we discussed the underlying details of TensorFlow architecture. Next, we discussed the essential ingredients of a meaningful TensorFlow client. Then we discussed a general coding practice widely used in TensorFlow known as scoping. Later, we brought all these elements together to implement a neural network to classify an MNIST dataset.

Specifically, we discussed the TensorFlow architecture lining up the explanation with an example TensorFlow client. In the TensorFlow client, we defined the TensorFlow graph. Then, when we created a session, it looked at the graph, created a GraphDef object representing the graph, and sent it to the distributed master. The distributed master looked at the graph, decided which components to use for the relevant computation, and divided it into several subgraphs to make the...

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Key benefits

  • • Focuses on more efficient natural language processing using TensorFlow
  • • Covers NLP as a field in its own right to improve understanding for choosing TensorFlow tools and other deep learning approaches
  • • Provides choices for how to process and evaluate large unstructured text datasets
  • • Learn to apply the TensorFlow toolbox to specific tasks in the most interesting field in artificial intelligence

Description

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.

Who is this book for?

This book is for Python developers with a strong interest in deep learning, who want to learn how to leverage TensorFlow to simplify NLP tasks. Fundamental Python skills are assumed, as well as some knowledge of machine learning and undergraduate-level calculus and linear algebra. No previous natural language processing experience required, although some background in NLP or computational linguistics will be helpful.

What you will learn

  • • Core concepts of NLP and various approaches to natural language processing
  • • How to solve NLP tasks by applying TensorFlow functions to create neural networks
  • • Strategies to process large amounts of data into word representations that can be used by deep learning applications
  • • Techniques for performing sentence classification and language generation using CNNs and RNNs
  • • About employing state-of-the art advanced RNNs, like long short-term memory, to solve complex text generation tasks
  • • How to write automatic translation programs and implement an actual neural machine translator from scratch
  • • The trends and innovations that are paving the future in NLP

Product Details

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Publication date : May 31, 2018
Length: 472 pages
Edition : 1st
Language : English
ISBN-13 : 9781788478311
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Publication date : May 31, 2018
Length: 472 pages
Edition : 1st
Language : English
ISBN-13 : 9781788478311
Category :
Languages :
Concepts :
Tools :

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Table of Contents

13 Chapters
1. Introduction to Natural Language Processing Chevron down icon Chevron up icon
2. Understanding TensorFlow Chevron down icon Chevron up icon
3. Word2vec – Learning Word Embeddings Chevron down icon Chevron up icon
4. Advanced Word2vec Chevron down icon Chevron up icon
5. Sentence Classification with Convolutional Neural Networks Chevron down icon Chevron up icon
6. Recurrent Neural Networks Chevron down icon Chevron up icon
7. Long Short-Term Memory Networks Chevron down icon Chevron up icon
8. Applications of LSTM – Generating Text Chevron down icon Chevron up icon
9. Applications of LSTM – Image Caption Generation Chevron down icon Chevron up icon
10. Sequence-to-Sequence Learning – Neural Machine Translation Chevron down icon Chevron up icon
11. Current Trends and the Future of Natural Language Processing Chevron down icon Chevron up icon
A. Mathematical Foundations and Advanced TensorFlow Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Victor Oct 07, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
With experience in Language Natural Processing I went into this book to introduce myself into TensorFlow. Definitely a good decision. The code is quite easy to follow, examples are useful and well explained. I recommend it.
Amazon Verified review Amazon
Deepal Bandaranayake Aug 27, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Natural language processing with Tensorflow is a very well-written book that gives a strong introduction to novel deep learning based NLP systems. With this book I've learned about word vectors, text generation, machine translation which are hot topics flying around at the moment.The book really dives into the details of implementing various NLP systems scanning through various TensorFlow functions involved in a modularized easy-to-follow manner. Each chapter is accompanied with Jupyter notebooks which again provide the full picture of the system end-to-end.If I'm to pick one particular example I liked in the book, I'd say it's the way the author describes the functioning of LSTMs. The author really brings the reader to his world and walk the reader through a easy-to-digest analogy of how an LSTM might operation, without much focus on mathematics. With that graceful entrance, he then continue to explain the LSTM in a mathematical perspective, which I found quite impressive.In conclusion, I genuinely enjoyed the book and think the book is a bang for bucks! I wouldn't hesitate this to another ML enthusiast looking for a good practical view of things!
Amazon Verified review Amazon
Tishan Jun 20, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book welcomes the reader to the modern deep learning based natural language processing techniques in a very progressive manner. Furthermore, the book often visualize elusive concepts with simpler explanations and colourful examples. For example, I liked the way Thushan discusses basics of TensorFlow and illustrates the workflow with a colourful example. Moreover, the book touches upon a multitude of NLP applications, providing a very diverse practical exposure to the current NLP solutions. I found the approach with more weight on the practicality and application, Thushan takes very appealing, in understanding the mechanics of various methods.
Amazon Verified review Amazon
Amazon Customer Sep 10, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The content covered in this book is excellent!!But binding quality of the publisher is very very poor. This happened for two books I purchased from Packet.
Amazon Verified review Amazon
Shirley Jun 29, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
it's useful~
Amazon Verified review Amazon
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