Deep learning for NLP using Python

In 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-

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