Deep Learning for Natural Language Processing - by Palash Goyal & Sumit Pandey & Karan Jain (Paperback)

Book Synopsis

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.
What You Will Learn

• Gain the fundamentals of deep learning and its mathematical prerequisites
  
• Discover deep learning frameworks in Python
  
• Develop a chatbot
  
• Implement a research paper on sentiment classification
  

Who This Book Is For
Software developers who are curious to try out deep learning with NLP.

From the Back Cover

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 indeep 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.
You will:

• Gain the fundamentals of deep learning and its mathematical prerequisites
  
• Discover deep learning frameworks in Python
  
• Develop a chatbot
• Implement a research paper on sentiment classification
  

About the Author

Palash Goyal works as Senior Data Scientist, and is currently working with the applications of Data Science and Deep Learning in Online Marketing domain. He studied Mathematics and Computing from IIT-Guwahati, and proceeded to work in a fast, upscale environment.He holds wide experience in E-Commerce, Travel, Insurance, and Banking industries. Passionate about mathematics and Finance, in his free time he manages his portfolio of multiple Cryptocurrencies and latest ICOs using Deep Learning and Reinforcement Learning techniques for price prediction and portfolio management.He keeps himself in touch with the latest trends in the Data Science field and pen it down on his personal blog and digs articles related to Smart Farming in left over time.
Sumit Pandey is a graduate from IIT Kharagpur. He worked for about a year with AXA Business services as a Data Science Consultant. He is currently engaged in launching his own venture.
Karan Jain is Product Analyst at Sigtuple, where he works on cutting edge AI driven diagnostic products . Before which he worked as a Data Scientist at Vitrana Inc, a healthcare solutions company.He enjoys working in fast culture and data-first start ups. In his leisure time he deeps dive into Genomics sciences, BCI interfaces, Optogenetics . He recently developed interest in POC devices and Nano tech for further portable diagnosis. He has healthy network of 3000+ followers on linkedin.