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Hands-On Natural Language Processing with Python

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  • 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

Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges.

To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow.

By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts.

  • Weave neural networks into linguistic applications across various platforms
  • Perform NLP tasks and train its models using NLTK and TensorFlow
  • Boost your NLP models with strong deep learning architectures such as CNNs and RNNs
Page Count 312
Course Length 9 hours 21 minutes
Date Of Publication 17 Jul 2018
Overview of speech recognition
Building an RNN model for speech recognition


Rajalingappaa Shanmugamani

Rajalingappaa Shanmugamani is currently working as a Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of Technology – Madras. He has published articles in peer-reviewed journals and conferences and applied for few patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

Rajesh Arumugam

Rajesh Arumugam is an ML developer at SAP, Singapore. Previously, he developed ML solutions for smart city development in areas such as passenger flow analysis in public transit systems and optimization of energy consumption in buildings when working with Centre for Social Innovation at Hitachi Asia, Singapore. He has published papers in conferences and has pending patents in storage and ML. He holds a PhD in computer engineering from Nanyang Technological University, Singapore.