Update README.md

Author: packtsnehalt

README.md

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 # Deep Learning for Natural Language Processing
-Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. 
+Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs.
+
 As you advance through this deep learning book, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search.
+
 By the end of this book, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues.
 
-# What you will learn
+## What you will learn
 Understand various pre-processing techniques for deep learning problems
 Build a vector representation of text using word2vec and GloVe
 Create a named entity recognizer and parts-of-speech tagger with Apache OpenNLP
 Build a machine translation model in Keras
 Develop a text generation application using LSTM
 Build a trigger word detection application using an attention model
 
-# Hardware Requirements
+## Hardware Requirements
 For the optimal student experience, we recommend the following hardware
 configuration:
 * Processor: Intel Core i5 or equivalent
 * Memory: 4 GB RAM
 * Storage: 5 GB available space
 
-# Software Requirements
+## Software Requirements
 We also recommend that you have the following software installed in advance:
 * OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Linux
 (Ubuntu, Debian, Red Hat, or Suse), or the latest version of OS X