Deci AI Model Optimization Techniques (eBook)

The Complete Guide for Developers and Engineers

William Smith (Autor)

eBook Download: EPUB

2025 | 1. Auflage
250 Seiten
HiTeX Press (Verlag)
978-0-00-102694-0 (ISBN)

Infos

'Deci AI Model Optimization Techniques'
Delve into the world of efficient deep learning with 'Deci AI Model Optimization Techniques,' a comprehensive exploration of modern strategies for maximizing AI model performance in real-world applications. This book offers a thorough foundation in the guiding principles of model optimization, blending theoretical underpinnings with application-driven best practices. Readers are introduced to core topics such as optimization objectives, model efficiency metrics, neural network compression, and the systemic constraints that arise when deploying AI across domains like vision, NLP, and recommendation systems.
Balancing depth and clarity, the text demystifies advanced subjects including automated neural architecture search (NAS), pruning, quantization, and knowledge distillation. Readers learn to harness Deci AI's cutting-edge AutoNAC engine for multi-objective optimization, adapt models for both edge and cloud environments, and combine multiple optimization techniques for truly efficient AI pipelines. The book guides practitioners through practical design considerations-such as hardware-aware optimization, hyperparameter tuning, and integration with MLOps workflows-while equipping them with robust benchmarking, profiling, and validation protocols to ensure reliability and reproducibility.
Looking toward the horizon, 'Deci AI Model Optimization Techniques' addresses emerging frontiers in automated model design, federated and distributed optimization, continual learning, and the ethical challenges at the heart of responsible AI deployment. Whether you are an engineer, researcher, or technical leader seeking to deploy scalable, high-performance AI, this book provides actionable insights and future-ready solutions for building and maintaining state-of-the-art models in an ever-evolving landscape.

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Chapter 2
Automated Neural Architecture Search (NAS) in Deci AI

Imagine discovering neural network architectures as powerful and tailored as the best human designs-but evolved automatically, at scale, and in tune with diverse operational needs. This chapter peels back the layers of neural architecture search (NAS) in Deci AI, revealing how automation, advanced optimization algorithms, and custom objectives converge to reshape what is possible for AI deployment. Venture beyond manual engineering into the frontier of machine-designed networks, where breakthrough performance is discovered in silico.

2.1 NAS Foundations and Algorithms

Neural Architecture Search (NAS) fundamentally addresses the problem of automating the design of neural network architectures to optimize performance for specific tasks. At its core, NAS formulates this as a search problem over a complex and often discrete space of potential architectures, balancing expressiveness, efficiency, and scalability. The search space definition directly influences both the quality of architectures discovered and the computational resources required. Consequently, understanding the construction of these spaces alongside the algorithms that traverse them is pivotal for advancing NAS methodologies.

Search Space Formulation

The search space in NAS constitutes all possible candidate architectures that an algorithm may consider. It is typically represented as a parameterized configuration set, where elements define architectural motifs such as layer types, connections, and hyperparameters. Formally, if

• Erscheint lt. Verlag: 20.8.2025
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
• ISBN-10: 0-00-102694-1 / 0001026941
• ISBN-13: 978-0-00-102694-0 / 9780001026940