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.
An introduction to various issues related to the theory and phenomenology of massive neutrinos for the nonexpert, also providing a discussion of results in the field for the active researcher. All the necessary techniques and logics are included and topics such as supersymmetry are covered.
The recent groundbreaking discovery of nonzero neutrino masses and oscillations has put the spotlight on massive neutrinos as one of the key windows on physics beyond the standard model as well as into the early universe. This third edition of the invaluable book Massive Neutrinos in Physics and Astrophysics is an introduction to the various issues related to the theory and phenomenology of massive neutrinos for the nonexpert, providing at the same time a complete and up-to-date discussion on the latest results in the field for the active researcher. It is designed not merely to be a guide but also as a self-contained tool for research with all the necessary techniques and logics included. Specially emphasized are the various implications of neutrino discoveries for the nature of new forces. Elementary discussions on topics such as grand unification, left-right symmetry and supersymmetry are presented. The most recent cosmological and astrophysical implications of massive neutrinos are also dealt with.
Identity Based Encryption (IBE) is a type of public key encryption and has been intensely researched in the past decade. Identity-Based Encryption summarizes the available research for IBE and the main ideas that would enable users to pursue further work in this area. This book will also cover a brief background on Elliptic Curves and Pairings, security against chosen Cipher text Attacks, standards and more. Advanced-level students in computer science and mathematics who specialize in cryptology, and the general community of researchers in the area of cryptology and data security will find Identity-Based Encryption a useful book. Practitioners and engineers who work with real-world IBE schemes and need a proper understanding of the basic IBE techniques, will also find this book a valuable asset.
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