Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.
In the past, while visiting the First World War battlefields, the author often wondered where the various Victoria Cross actions took place. He resolved to find out. In 1988, in the midst of his army career, research for this book commenced and over the years numerous sources have been consulted. Victoria Crosses on the Western Front 1917 to Third Ypres is designed for the battlefield visitor as much as the armchair reader. A thorough account of each VC action is set within the wider strategic and tactical context. Detailed sketch maps show the area today, together with the battle-lines and movements of the combatants. It will allow visitors to stand upon the spot, or very close to, where each VC was won. Photographs of the battle sites richly illustrate the accounts. There is also a comprehensive biography for each recipient, covering every aspect of their lives warts and all parents and siblings, education, civilian employment, military career, wife and children, death and burial/commemoration. A host of other information, much of it published for the first time, reveals some fascinating characters, with numerous links to many famous people and events.
This text provides a single-volume, single-author general introduction to the Celtic languages. The first half of the book considers the historical background of the language group as a whole. There follows a discussion of the two main sub-groups of Celtic, Goidelic (comprising Irish, Scottish, Gaelic and Manx) and Brittonic (Welsh, Cornish and Breton) together with a detailed survey of one representative from each group, Irish and Welsh. The second half considers a range of linguistic features which are often regarded as characteristic of Celtic: spelling systems, mutations, verbal nouns and word order.
Lloyd presents an historical grammar of Spanish that includes 20th-century research on Romance and Spanish languages. He offers a synthesis of the research that has illuminated much of the phonetic and morphological development of Spanish.
This is a primer written for computer architects in the new and rapidly evolving field of deep learning. It reviews how machine learning has evolved since its inception in the 1960s and tracks the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. It also reviews representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, it also details the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, it presents a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.
Good Day! , the critically-acclaimed biography about the legendary Paul Harvey, is now in paperback! In this heartwarming book, author Paul J. Batura tells the all-American story of one of the best-known radio voices in history. From his humble beginnings to his unparalleled career of more than 50 years with ABC radio, Paul Harvey narrated America's story day by day, through wars and peace, through the threat of communism and the crumbling of old colonial powers, through consumer booms and eventual busts.
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