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 LearnGain 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.
Number of pages: 277
Weight: 4511 g
Dimensions: 235 x 155 mm
Edition: 1st ed.
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