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Are you thinking of learning more Natural Language Processing (NLP) using TensorFlow?

This book is for you. It would seek to explain common terms and algorithms in an intuitive way. The authors used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. This book and the accompanying examples, you would be well suited to tackle problems which pique your interests using ¨NLP.

From AI Sciences Publisher

Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach which would lead to better mental representations.

Target Users

The book designed for a variety of target audiences. The most suitable users would include:

• Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field.
• Software developers and engineers with a strong programming background but seeking to break into the field of Data Science and NLP.
• Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird’s eye view of current techniques and approaches.

What’s Inside This Book?

• Introduction to Natural Language Processing
• What is Natural Language Processing
• Perspectivizing NLP: Areas of AI and Their Interdependencies
• Purpose of Natural Language Processing
• Text Manipulation
• Tokenization
• Stemming
• Lemmatization
• Normalization
• Accessing Text Corpora and Lexical Resources
• Processing Raw Text
• Categorizing and Tagging Words
• NLP Applications
• Text Classification
• Sentiment Classification
• Topic Modelling
• Question Answering
• Speech Recognition
• Machine Translation
• Word Representation
• Bag of Words
• One-Hot Encoding
• Word Vectors Representation
• Word2Vec and GloVe
• Learning to Classify Text
• Supervised Classification
• Decision Trees
• Naive Bayes Classifiers
• Maximum Entropy Classifiers
• Deep Learning for NLP
• What is Deep Learning
• Feed Forward Neural Networks
• Recurrent Neural Networks
• Gated Recurrent Unit
• Long Short Term Memory
• Language Processing and Python using NLTK
• Introduction to TensorFlow Text Classification

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Frequently Asked Questions

Q: Is this book for me and do I need programming experience? A: If you want to smash NLP from scratch, this book is for you. If you already wrote a few lines of code and recognize basic programming statements, you’ll be OK.

Q: Does this book include everything I need to become a NLP expert? A: Unfortunately, no. This book is designed for readers taking their first steps in NLP and further learning will be required beyond this book to master all aspects of NLP.

Q: Can I have a refund if this book doesn’t fit for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at contact@aisciences.net.