Foster your NLP applications with the help of deep learning, NLTK, and TensorFlow Key Features Weave neural networks into linguistic applications across various platforms Perform NLP tasks and train its models using NLTK and TensorFlow Boost your NLP models with strong deep learning architectures such as CNNs and RNNs Book Description Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges. To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts. What you will learn Implement semantic embedding of words to classify and find entities Convert words to vectors by training in order to perform arithmetic operations Train a deep learning model to detect classification of tweets and news Implement a question-answer model with search and RNN models Train models for various text classification datasets using CNN Implement WaveNet a deep generative model for producing a natural-sounding voice Convert voice-to-text and text-to-voice Train a model to convert speech-to-text using DeepSpeech Who this book is for Hands-on Natural Language Processing with Python is for you if you are a developer, machine learning or an NLP engineer who wants to build a deep learning application that leverages NLP techniques. This comprehensive guide is also useful for deep learning users who want to extend their deep learning skills in building NLP applications. All you need is the basics of machine learning and Python to enjoy the book.
Genitourinary Imaging - A Case Based Approach is a user friendly, portable guide that offers a comprehensive coverage of the subspecialty with an emphasis on all modalities. As subspecialty practice has become the norm in radiology, it becomes increasingly important to keep abreast of the latest information within the specialty that one is practising. The case based nature of this book, with modality independent content, allows the reader to assimilate practice specific information with ease. Full color images with tabular salient points, imaging algorithms and evidence based criterion support the text. Key points provide succinct explanations of the disease and an appropriate differential diagnosis, as well as providing a brief description of therapy and prognosis. Genitourinary Imaging - A Case Based Approach is aimed at residents and training and practicing genitourinary radiologists.
Foster your NLP applications with the help of deep learning, NLTK, and TensorFlow Key Features Weave neural networks into linguistic applications across various platforms Perform NLP tasks and train its models using NLTK and TensorFlow Boost your NLP models with strong deep learning architectures such as CNNs and RNNs Book Description Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges. To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts. What you will learn Implement semantic embedding of words to classify and find entities Convert words to vectors by training in order to perform arithmetic operations Train a deep learning model to detect classification of tweets and news Implement a question-answer model with search and RNN models Train models for various text classification datasets using CNN Implement WaveNet a deep generative model for producing a natural-sounding voice Convert voice-to-text and text-to-voice Train a model to convert speech-to-text using DeepSpeech Who this book is for Hands-on Natural Language Processing with Python is for you if you are a developer, machine learning or an NLP engineer who wants to build a deep learning application that leverages NLP techniques. This comprehensive guide is also useful for deep learning users who want to extend their deep learning skills in building NLP applications. All you need is the basics of machine learning and Python to enjoy the book.
Genitourinary Imaging - A Case Based Approach is a user friendly, portable guide that offers a comprehensive coverage of the subspecialty with an emphasis on all modalities. As subspecialty practice has become the norm in radiology, it becomes increasingly important to keep abreast of the latest information within the specialty that one is practising. The case based nature of this book, with modality independent content, allows the reader to assimilate practice specific information with ease. Full color images with tabular salient points, imaging algorithms and evidence based criterion support the text. Key points provide succinct explanations of the disease and an appropriate differential diagnosis, as well as providing a brief description of therapy and prognosis. Genitourinary Imaging - A Case Based Approach is aimed at residents and training and practicing genitourinary radiologists.
Nanotechnology in Textiles: Theory and Application explains how conventional methods for treating fabrics for specific functions can by improved upon with the use of nanotechnology. Overviews of relevant, fundamental nanophysics and nanochemistry theory are provided, along with explanations of their application in textile finishing, providing a crucial resource for readers exploring this expanding frontier in textiles. The book draws on research from around the globe to address the latest nanotechnological developments that are all examined with references to industrial applications. Provides a complete, theoretical overview of nanotechnology and nanofibers for those with materials science or engineering backgrounds Covers a broad range of topics, including aerogels, polymer nanocomposites, nanohazards, and electrospinning Looks ahead to emerging applications of nanotechnology in textiles to point the way for further research and innovation
In this partnership between so-called equals, which can be compared to a polyandrous marriage, the Supreme Court is the woman and Parliament and the Executive her two husbands, one more loutish that the other, depending on your point of view. In the Nirbhaya case too the gap between theory and law has been highlighted. Following the terrible episode, (and even before) there has been continual and great improvement in the substantive laws for both women as well as children who have been victims of sexual violence. And yet despite their being so much publicity on the case, the author argues that, concretely, although there has been improvement in the laws themselves, we are nowhere near better enforcement or implementation. Even after the institution of a fast track trial, and with the nation’s attention focused on it, the Nirbhaya case still dragged on and it took more than nine months for the trial court to reach a verdict. And, as the author explains there are still potentially further delays waiting at the level of the superior courts, the High Court certainly and the Supreme Court too, quite possibly. As the author goes on to show in this well argued book, a woman who is the victim of a sex related crime ‘courts injustice’ whenever she comes to a court, be she the victim of a rape, an acid attack, of sexual harassment; the mother or father of such a victim or be it even any ordinary person struggling to find justice. Our courts, particularly the Supreme Court is performing the function of a nagging wife. Time and again she pulls up the lazy, good-for-nothing husbands (read ‘failure of governance’). And what does either husband do? He goes for a walk, ignoring the wife’s anguished screams even as they follow him. If she complains too much, he tells himself, he’ll see to it that she doesn’t get the silk sari and other goodies she wants (read ‘promotions’, ‘post retirement assignments’, etc). It is only one of the ways he ensures that she doesn’t step too much out of line. All wives nag, he consoles himself. Nagging here and there is tolerable but she must make sure that he gets his meals on time (read ‘doesn’t bar him from contesting elections even if there are a dozen or more criminal cases pending against him’). Meanwhile the overzealous wife doesn’t realize that while she rails and rants against the erring ways of her husband, the dishes are piling up in the kitchen. And the maid has gone away for six months and the dishes, they are piling up (read, the arrears are accumulating)! The time has come. It cannot continue to remain ‘business as usual’. There will be justice for Nirbhaya. Our ‘brave heart’ will also bring justice and relief to all her sisters. And possibly, even to the rest of us.
Xenobiotics in Chemical Carcinogenesis: Translational Aspects in Toxicology covers the translational toxicology of xenobiotics substances in carcinogenesis by explaining the toxicokinetic and toxicodynamic, toxicogenomic, biotransformation, and resistance mechanisms in the human body. The book begins with a historical review and link to future prospects for chemical carcinogenesis. It discusses major environmental xenobiotics and their risks in inducing cancer, along with content on toxic xenobiotics and their routes of exposure in humans, the role of xenobiotic metabolism in carcinogenesis, and the toxicokinetic and toxicodynamic of xenobiotics in cancer development. Lastly, the book explores current achievements such as using toxicogenomics for predicting the carcinogenicity of xenobiotic substances and the challenges posed by carcinogenic xenobiotic substances when examining preventive methods, diagnosis, and the development of anticancer drugs for specific toxicants. Covers the exposure and transmission of various toxic xenobiotics substances, including nanomaterials, to humans and their interaction with specific tissues in precipitating the development of cancers Unravels the toxicokinetic and toxicodynamic processes of toxic xenobiotics in bioaccumulation Examines the genetic aberrations in cancer genomes by genetic-environmental interactions in carcinogenesis Explains the biotransformation mechanisms of toxic xenobiotics by gut microbes in humans
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