This book attempts to simplify and present the concepts of deep learning
in a very comprehensive manner, with suitable, full-fledged examples of
neural network architectures, such as Recurrent Neural Networks (RNNs)
and Sequence to Sequence (seq2seq), for Natural Language Processing
(NLP) tasks. The book tries to bridge the gap between the theoretical and
the applicable.
It proceeds from the theoretical to the practical in a progressive
manner, first by presenting the fundamentals, followed by the underlying
mathematics, and, finally, the implementation of relevant examples.
The first three chapters cover the basics of NLP, starting with the most
frequently used Python libraries, word vector representation, and then
advanced algorithms like neural networks for textual data.
The last two chapters focus entirely on implementation, dealing with
sophisticated architectures like RNN, Long Short-Term Memory (LSTM)
Networks, Seq2seq, etc., using the widely used Python tools TensorFlow
and Keras. We have tried our best to follow a progressive approach,
combining all the knowledge gathered to move on to building a questionand-
answer system.
The book offers a good starting point for people who want to get
started in deep learning, with a focus on NLP.
All the code presented in the book is available on GitHub, in the form
of IPython notebooks and scripts, which allows readers to try out these
examples and extend them in interesting, personal ways.
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