This book shows how to use deep learning models for performing various Natural Language Processing tasks, along with best practices in dealing with today’s NLP challenges. Authors Rajesh Arumugam and Rajalingappaa Shanmugamani begin with core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding and Word2vec. They show how to use RNNs and CNNs in various application areas, such as text classification and sequence labeling and also cover the use of TensorFlow in implementing deep learning in linguistic applications.

<ASIN:178913949X>

Authors: Rajesh Arumugam and Rajalingappaa Shanmugamani
Publisher: Packt
Date: July 2018
Pages: 312
ISBN: 978-1789139495
Print: 178913949X
Kindle: B07D6KYQYP
Audience: Python developers interesting in natural language processing
Level: Intermediate
Category: Artificial Intelligence and Python 

• Implement semantic embedding of words to classify and find entities
• Convert words to vectors by training in order to perform arithmetic operations
• Train a deep learning model to detect classification of tweets and news
• Implement a question-answer model with search and RNN models
• Train models for various text classification datasets using CNN
• Implement WaveNet a deep generative model for producing a natural-sounding voice
• Convert voice-to-text and text-to-voice
• Train a model to convert speech-to-text using DeepSpeech

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