to optimize machine learning models for deployment on resource-constrained hardware. This book explores methods such as pruning, quantization, and knowledge distillation to reduce model size while maintaining accuracy. It emphasizes the importance of efficient models in enabling fast, reliable voice search on mobile and embedded devices. The discussion underscores the balance between performance and computational limitations in portable voice recognition systems.

Continental Academy Press

Editore: Continental Academy Press, London

Riguardo questo articolo

model compression, mobile devices, voice search, machine learning, optimization Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

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• Titolo: to optimize machine learning models for deployment on resource-constrained hardware. This book explores methods such as pruning, quantization, and knowledge distillation to reduce model size while maintaining accuracy. It emphasizes the importance of efficient models in enabling fast, reliable voice search on mobile and embedded devices. The discussion underscores the balance between performance and computational limitations in portable voice recognition systems.
• Editore: Continental Academy Press, London
• Rilegatura: Softcover
• Edizione: First.
• Stampa: 1st printing
• Condizione: New
• Condizione della sovraccoperta: no dj