Ce Zhang – författare

The 10 revised full papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in topical sections named: Architecture and System, Algorithm and Dataset, Network and Memory.

This book explores the history of mechanical engineering since the Bronze Age. Focusing on machinery inventions and the development of mechanical technology, it also discusses the machinery industry and modern mechanical education. The evolution of machinery is divided into three stages: Ancient (before the European Renaissance), Modern (mainly including the two Industrial Revolutions) and Contemporary (since the Revolution in Physics, especially post Second World War). The book not only clarifies the development of mechanical engineering, but also reveals the driving forces behind it – e.g. the economy, national defense and human scientific research activities – to highlight the links between technology and society; mechanical engineering and the natural sciences; and mechanical engineering and related technological areas.

Though mainly intended as a textbook or supplemental reading for graduate students, the book also offers a unique resource for researchers and engineers inmechanical engineering who wish to broaden their horizons.

This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol.

Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appealto a broad audience in the field of machine learning, artificial intelligence, big data and database management.