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Deep Learning for Natural Language Processing: Creating Neural Networks with Python By Palash Goyal, Sumit Pandey PDF

Download Books : Deep Learning for Natural Language Processing: Creating Neural Networks with Python By Palash Goyal, Sumit Pandey PDF




Informations about the book:




Title: Deep Learning for Natural Language Processing: Creating Neural Networks with Python

Author: Palash Goyal, Sumit Pandey

Size: 7.4 MB

FormatPDF

Year2018

Pages290


Book Contents:




Chapter 1: Introduction to Natural Language Processing and  Deep Learning
Python Packages
Introduction to Natural Language Processing
Natural Language Processing Libraries
Getting Started with NLP
Introduction to Deep Learning
What Are Neural Networks?
Basic Structure of Neural Networks
Types of Neural Networks 
Multilayer Perceptrons
Stochastic Gradient Descent
Backpropagation
Deep Learning Libraries
Next Steps
Chapter 2: Word Vector Representations
Introduction to Word Embedding
Word2vec
Subsampling Frequent Words
Word2vec Code
Skip-Gram Code
CBOW Code
Next Steps 
Chapter 3: Unfolding Recurrent Neural Networks
Recurrent Neural Networks
Recurrent Neural Network Basics
Natural Language Processing and Recurrent Neural Networks
RNNs Mechanism
Training RNNs
Meta Meaning of Hidden State of RNN
Tuning RNNs
Long Short-Term Memory Networks
Sequence-to-Sequence Models
Advanced Sequence-to-Sequence Models
Sequence-to-Sequence Use Case
Chapter 4: Developing a Chatbot
Introduction to Chatbot
Conversational Bot
Chatbot: Automatic Text Generation
Chapter 5: Research Paper Implementation: Sentiment Classification
Self-Attentive Sentence Embedding
Implementing Sentiment Classification
Sentiment Classification Code
Model Results
Scope for Improvement




Description:


Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models.
You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system.
This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways




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