by Tyler Edwards | Released May 2018 | ISBN: 9781788621700

Learn how to apply the concepts of deep learning to a diverse range of natural language processing (NLP) techniquesAbout This VideoUse NLP to process raw text with NLTKFind useful information from piles of text using NLP through NLTKWork with a text corpus, conditional frequency distribution, and a lexical resource for statistical analysis and hypothesis testingIn DetailIn this course, you'll expand your NLP knowledge and skills while implementing deep learning tools to perform complex tasks. You'll start by preparing your environment for NLP and then quickly learn about language structure and how we can break sentences down to extract information and uncover the underlying meaning. After reviewing the basics, we'll move on to speech recognition and show how deep learning can be used to build speech recognition applications.In order to give you the best hands-on experience, the course includes a wide variety of practical real world examples. You'll discover how a Naive Bayes algorithm can be used for Binary and Multiclass text classification. We'll show you how a binary classifier can be used to determine if a product review would best be classified as positive or negative. You'll also learn how document classifiers can be used to predict information about the author of a text like their age, gender, or where they're from.Finally speech recognition systems will be introduced and you'll learn how to apply deep learning techniques to build your own speech to text application. We'll walk through two examples, step-by-step, showing how to build and train neural networks to understand spoken audio inputs.By the end of this tutorial, you'll have a better understanding of NLP and will have worked on multiple examples that implement deep learning to solve real-world spoken language problems. In particular, you'll be able to discover useful information and extract key insights from piles of natural language data. All the code and supporting files for this course are available on Github at: https://github.com/PacktPublishing/Deep-learning-for-NLP-using-Python-v- Show and hide more

1. Chapter 1 : Working with a Text Corpus
   • The Course Overview 00:04:00
   • Access and Use the Built-in Corpora of NLTK 00:06:20
   • Loading a Corpus 00:04:08
   • An Example of Conditional Frequency Distribution 00:05:12
   • An Example of a Lexical Resource 00:06:40
2. Chapter 2 : Processing Raw Text with NLTK
   • Working with an NLP Pipeline 00:06:14
   • Implementing Tokenization 00:05:31
   • Regular Expressions 00:05:31
   • Regular Expressions Used in Tokenization 00:05:57
3. Chapter 3 : A Practical Real World Example of Text Classification
   • Naive Bayes Text Classification 00:07:07
   • Age Prediction Application 00:06:37
   • Document Classifier Application 00:05:55
4. Chapter 4 : Finding Useful Information from Piles of Text
   • Hierarchy of Ideas or Chunking 00:02:34
   • Chunking in Python NLTK 00:05:19
   • Chinking (Non-Chunk Patterns) in NLTK 00:05:34
5. Chapter 5 : Developing a Speech to Text Application Using Python
   • Python Speech Recognition Module 00:06:12
   • Speech to Text with Recurrent Neural Networks 00:09:37
   • Speech to Text with Convolutional Neural Networks – Part One 00:06:30
   • Speech to Text with Convolutional Neural Networks – Part Two 00:06:28
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