| 书目名称 | Deep Learning for Computer Architects |
| 编辑 | Brandon Reagen,Robert Adolf,David Brooks |
| 视频video | http://file.papertrans.cn/265/264605/264605.mp4 |
| 丛书名称 | Synthesis Lectures on Computer Architecture |
| 图书封面 |  |
| 描述 | .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 workloadsthemselves, 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 dedicat |
| 出版日期 | Book 2017 |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-01756-8 |
| isbn_softcover | 978-3-031-00628-9 |
| isbn_ebook | 978-3-031-01756-8Series ISSN 1935-3235 Series E-ISSN 1935-3243 |
| issn_series | 1935-3235 |
| copyright | Springer Nature Switzerland AG 2017 |
|Archiver|手机版|小黑屋|
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