Surya Home Collection

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This timely book covers various aspects of abiotic stress-resilient plants, including stress responses in plants and the progress made so far in the trait improvements, as well as integrating knowledge gained from basic physiology and discussing key genes, proteins, and metabolites for developing improved crop varieties.

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This book is a compilation of 17 information-packed chapters authored by working scientists in their respective discipline. In addition to the theoretical information, practical and applied aspects to boost the productivity and quality of crops are also given.

Note: T&F does not sell or distribute the Hardback in India, Pakistan, Nepal, Bhutan, Bangladesh and Sri Lanka. This title is co-published with NIPA.

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Plants often encounter abiotic stresses including drought, salinity, flooding, high/low temperatures, and metal toxicity, among others. The majority of these stresses occur simultaneously and thus limit crop production. Therefore, the need of the hour is to improve the abiotic stresses tolerance of crop plants by integrating physiology, omics, and modern breeding approaches. This book covers various aspects including (1) abiotic stress responses in plants and progress made so far in the allied areas for trait improvements, (2) integrates knowledge gained from basic physiology to advanced omics tools to assist new breeding technologies, and (3) discusses key genes, proteins, and metabolites or pathways for developing new crop varieties with improved tolerance traits.

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If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving