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

Ganegedara

Saad

€8.99 ~~€26.99~~

4.5 (10 Ratings)

eBook May 2018 472 pages 1st Edition

Ganegedara

Saad

€8.99 ~~€26.99~~

4.5 (10 Ratings)

eBook May 2018 472 pages 1st Edition

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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...

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...

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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

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Hands-On Natural Language Processing with Python

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Natural Language Processing and Computational Linguistics

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Victor Oct 07, 2018

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

Deepal Bandaranayake Aug 27, 2018

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!

Tishan Jun 20, 2018

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 Customer Sep 10, 2018

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.

Shirley Jun 29, 2019

it's useful~

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

What do you get with eBook?

Natural Language Processing with TensorFlow

Chapter 2. Understanding TensorFlow

What is TensorFlow?

Inputs, variables, outputs, and operations

Defining inputs in TensorFlow

Reusing variables with scoping

Implementing our first neural network

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