This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.
Indian Writing In English Has Undoubtedly Acquired Its Own Independent Identity; It No More Remains Mere Imitative And Derivative. Its Long Journey From Colonial To Post-Colonial, From Imperial To Democratic And From English To Hinglish Forms A Remarkable Chapter In The History Of World Literature. Tagore Earned The First Recognition And Naipaul Is The Recent Laureate. In Between These Nobel Laureates Came A Number Of Writers Whose Work Earned Worldwide Appreciation.The Present Book Is An Attempt To Present The Different Genres Of Indian Writing In English. It Aims At Tracing Its Distinctive Features, Such As Cultural Alienation, Romanticism, Realism, Naturalism, Modernism Etc. While Nehru Has Furnished The Best English To The Globe, Amitav Ghosh, Shashi Tharoor, Arundhati Roy, Shiv K. Kumar And Dattani Have Stirred The West With Their Great Works. The Works Of These Renowned Literary Figures Have Been Considered Thoroughly And Meticulously In The Present Book.It Is Hoped That While The Student Community Will Find It Easily Accessible, The Teachers Will Also Consider It Exciting Study Material.
This book provides a unique ethnographic account of women living with polycystic ovary syndrome (PCOS) in India. It examines how contaminated environments and political–economic changes render urban middle-class women in India vulnerable to PCOS, a condition which has the potential to disrupt conventional, normative feminine biographies of marriage and childbearing. The volume revolves around two main themes: how toxic landscapes, the endocrine disrupting chemicals suffusing them, and the political–economic environments related to them are linked to endocrine disorders such as PCOS; and how the biosocial disruptions caused by PCOS are both affecting women and reflective of changes in contemporary urban India. The author draws on anthropological fieldwork to investigate these connections through a fresh approach, combining a political ecological framework with perspectives from the anthropology of toxic exposures and health–environment systems. The first of its kind, this volume will be indispensable to students and researchers of anthropology, particularly medical anthropology, medical sociology, human geography, science and technology studies, medical humanities, health–environment systems, endocrine disorders, public health, and South Asian studies.
The 2nd and 3rd waves of the pandemic had globally hit the people harder than the 1st wave and has left behind deaths, fears, orphans, widows, bankruptcies, pains, helplessness and tears for people. Adv. Gauri Chhabria, in her conversations with 22 renowned people, has written in this book about their entire journey in the last 2 years. She has showcased how they have proved themselves as one of the best soldiers to fight the war. When it rains, look for rainbows. When it’s dark, look for stars. Jai Hind!
Heavy metal pollution due to various industrial and agricultural activities is causing serious threats both to terrestrial and aquatic ecosystems. This book provides critical research analysis on the role of mycorrhiza on microbial enzymes, improvement of growth of seedlings under heavy metal polluted soil and the possible mechanism of detoxification of heavy metals by microbes, mycorrhiza and plants.
This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.
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