Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters.
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- Algorithms & data structures
- artificial intelligence
- Big Data and Machine Learning
- Computer networking & communications
- Computer programming / software development
- Computer science
- Computing & information technology
- Data mining for Ubiquitous System Software Cyber-physical systems Machine learning in high-energy physics Machine learning for knowledge discovery
- Embedded Systems and Machine Learning
- Highly Distributed Data
- Mathematics & science
- ML on Small devices
- Resource-Aware Machine Learning
- Resource-Constrained Data Analysis
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