Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues. Key FeaturesGain insights into the basic building blocks of natural language processingLearn how to select the best deep neural network to solve your NLP problemsExplore convolutional and recurrent neural networks and long short-term memory networksBook Description 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 learnUnderstand various pre-processing techniques for deep learning problemsBuild a vector representation of text using word2vec and GloVeCreate a named entity recognizer and parts-of-speech tagger with Apache OpenNLPBuild a machine translation model in KerasDevelop a text generation application using LSTMBuild a trigger word detection application using an attention modelWho this book is for If you’re an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must.
Annotation Provides administrators with in depth coverage of the #1 UNIX operating system, including features new to the popular version 9. Provides real-life exposure to the reader for the most popular UNIX operating system. Covers new features of Solaris such as NIS/NIS+ (Network Information Service) and support for IPv6. Information is presented in a concise language and provides graphical cues for clear understanding of the features covered. Solaris 9 is a soon-to-be-released UNIX operating system by Sun Microsystems that enables organizations to improve services, reduce risks, and decrease costs through the promise of scalability, continuous real-time computing, and enhanced security. With the release of Solaris 9, Sun Microsystems has defined what a dot-com grade platform must be. It has been tested in the worlds largest data centers. Special Edition Using Solaris 9 is intended to be a one-stop, in-depth reference guide to Solaris 9. It focuses on the reader gaining real-life exposure to the functions of the operating system. This book avoids jargon, and emphasizes brevity and clarity. It also covers key features of Solaris 9 such as NIS/NIS+, Solstice DiskSuie, Solstice AdminSuite, WebFS, JumpStart, and WebStart. Ganesh Govindaswamy is a Sun Certified Solaris System Administrator and is currently a Solaris System Administrator at InfoBase Technologies Ltd., where he administers user accounts, file systems, and network services. He is also certified in C and C++. NIIT is a global training and software organization offering customized and packaged multimedia educational software products and training. NIIT and Sun Microsystems have a Master Services Agreement. NIIT handles the entire enterprise-wide support functions for Sun, including Sun Technical support, Sun consulting services, training and certification. NIIT has one of the largest learning material development facilities in the world. 0789726505
Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues. Key FeaturesGain insights into the basic building blocks of natural language processingLearn how to select the best deep neural network to solve your NLP problemsExplore convolutional and recurrent neural networks and long short-term memory networksBook Description 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 learnUnderstand various pre-processing techniques for deep learning problemsBuild a vector representation of text using word2vec and GloVeCreate a named entity recognizer and parts-of-speech tagger with Apache OpenNLPBuild a machine translation model in KerasDevelop a text generation application using LSTMBuild a trigger word detection application using an attention modelWho this book is for If you’re an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must.
Saransh works at a life insurance company, as part of the Special Projects Group (SPG). Their current project is top-secret: the development of an Artificial Intelligence system that will leave 552 branch-level employees redundant overnight. Because of site-specific customizations, however, the system needs to collect information from the company’s various branches. Thus, begins a cycle in which Saransh travels across the country, interviewing the very people that his machine will replace soon. Meanwhile, his conscientious ex-journalist girlfriend Jyoti repeatedly questions Saransh’s complicity in the impending destruction of hundreds of lives. The Machine is Learning is a novel about twenty-first-century workplaces, love and the impact of technology in all of our lives. It interrogates a world order that accommodates guilt but offers no truly ethical course correction.
Foundational digital public infrastructure (DPI), consisting of unique digital identification, payments system and data exchange layer has the potential to support the transformation of the economy and support inclusive growth. India’s foundational DPI, called India Stack, has been harnessed to foster innovation and competition, expand markets, close gaps in financial inclusion, boost government revenue collection and improve public expenditure efficiency. India’s journey in developing a world-class DPI highlights powerful lessons for other countries embarking on their own digital transformation, in particular a design approach that focuses on shared building blocks and supporting innovation across the ecosystem.
The Handbook on Forensic Nursing is a comprehensive guide that bridges the gap between healthcare and the legal system in India. Written by a team of experienced forensic nursing professionals and medical jurisprudence experts, this handbook serves as an invaluable resource for nursing students, nurses, healthcare professionals and legal professionals. Salient Features Simple and lucid content: This handbook presents contents comprehensively in simple, lucid manner to meet all the needs of undergraduate nursing students. Easy-to-follow: This is an applied, user-friendly handbook with self-explanatory simple language and presentation for the readers. Fused on required content: The handbook is based on the new curriculum prescribed for Introduction to Forensic Nursing & Indian Laws by Indian Nursing Council. Authentic content: The content has been contributed and reviewed by renowned forensic nursing professionals, and forensic and medical jurisprudence experts in India. Enormous knowledge in small handbook: The handbook provides in-depth coverage of all aspects of forensic nursing and Indian laws in a concise manner. A ready reference: Whether you are a forensic nurse, healthcare professional, legal expert or law enforcement officer, this handbook will equip you with the knowledge and skills needed to navigate the complexities of forensic nursing within Indian legal system.
He is a bruised man, adrift, keening for a lost love. His sorrow submerges everything: his agony is truest, his epiphanies greatest. Do you despise him? You're too late. He despises himself already.This is his story: Anne-Marie, his true love, has left him and their Mumbai flat. There is a girl who pretends to be a lesbian with whom he has an awkward encounter of the almost-coital kind. And then, when he goes to Pattaya looking for sex (when he could have gone to Interlaken looking for love), he finds Noon, just the sort of woman who might mend - and break again - his wounded heart; and he finds Orhan, who may or may not be the son he never had.Here is a debut at once pensive and feral, cutting down to our most private tragedies - and to that shameful inference we must all some day come to: we are neither heroes nor insects.
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