About the course
Learn how to apply the concepts of deep learning to a diverse range of natural language processing (NLP) techniques
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-
Style and Approach
In this course you will be familiarized with how deep learning can be applied for natural language processing in Python. The course is compartmentalized in a manner that it would allow you to progress at your own pace
What You Will Learn
- Learn how to build speech to text applications using deep learning.
- Implement deep learning with a convolution neural network, and a recurrent neural network using long-short term memory
- See how you can load, access, and use the built-in corpora of NLTK for linguistic research
- Create conditional frequency distributions for a given text dataset
- Utilize a lexical resource to organize text data and create relationships
- Process raw text with NLTK by implementing an NLP pipeline and implementing tokenization
- Use document classification algorithms to extract information about a text like the age and sentiment of the author
- Discover how the Naive-Bayes algorithm can be used for Binary and Multiclass text classification
- Understand the concepts of hierarchy of ideas, chunking, and chinking
- Use NLP to reduce long strings of information that can be difficult to analyze down into shorter, more manageable chunks of text data
Tyler Edwards is a senior engineer and software developer with over a decade of experience creating analysis tools in the space, defense, and nuclear industries. Tyler is experienced using a variety of programming languages (Python, C++, and more), and his research areas include machine learning, artificial intelligence, engineering analysis, and business analytics. Tyler holds a Master of Science degree in Mechanical Engineering from Ohio University. Looking forward, Tyler hopes to mentor students in applied mathematics, and demonstrate how data collection, analysis, and post-processing can be used to solve difficult problems and improve decision making.
Applications of Statistical Learning with Python