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Klein, Aber Oho!: Edge-KI-Engineering: Quantisierung, Pruning und On-Device-ML für eingebettete Systeme
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Publisher: Independently Published
Pages: 312
Weight: 0.83
Height: 0.78 Width: 6.00 Depth: 9.00
Language: German
KLEIN, ABER OHO! ist ein praxisorientierter Leitfaden für KI-Ingenieure, Embedded-Entwickler, Machine-Learning-Spezialisten und Robotikexperten, die leistungsfähige KI-Modelle direkt auf eingebetteten Geräten ausführen möchten.
Edge-KI bringt maschinelles Lernen dorthin, wo Daten entstehen: auf Sensoren, Mikrocontroller, mobile Geräte, Industriehardware und autonome Systeme. Doch begrenzter Speicher, geringe Rechenleistung und niedriger Energieverbrauch stellen besondere Anforderungen an Modelle und Software. Dieses Buch zeigt, wie KI-Modelle gezielt optimiert und für den zuverlässigen On-Device-Betrieb vorbereitet werden.
Von Quantisierung und Pruning bis hin zu effizienter Inferenz und On-Device-ML vermittelt dieses Werk praxisnahe Methoden für moderne Edge-KI-Systeme.
Du lernst unter anderem:
- Wie Machine-Learning-Modelle für eingebettete Hardware optimiert werden
- Wie Quantisierung zur Reduzierung von Modellgrö e und Rechenaufwand eingesetzt wird
- Wie Pruning überflüssige Modellparameter entfernt
- Wie effiziente On-Device-Inferenz entwickelt und implementiert wird
- Wie Speicherverbrauch, Latenz und Energiebedarf optimiert werden
- Wie Edge-KI-Modelle auf Mikrocontrollern und Embedded-Systemen eingesetzt werden
- Wie Modelle hinsichtlich Genauigkeit, Geschwindigkeit und Ressourcenverbrauch bewertet werden
- Wie KI-Pipelines für autonome Geräte und produktive Edge-Anwendungen entwickelt werden
Praxisnahe Beispiele, Optimierungsstrategien und reale Anwendungsszenarien vermitteln das Wissen, um kompakte KI-Modelle zu entwickeln, die trotz begrenzter Hardware leistungsfähig und zuverlässig arbeiten.
Ob intelligente Sensoren, Robotik, industrielle Automatisierung, IoT-Geräte, Kamerasysteme oder autonome Maschinen - dieses Buch vermittelt die Fähigkeiten, um moderne Edge-KI direkt auf Geräten einzusetzen, ohne ständig auf leistungsstarke Cloud-Infrastrukturen angewiesen zu sein.
KLEIN, ABER OHO! - Der umfassende Leitfaden für Edge-KI-Engineering, Quantisierung, Pruning und On-Device-Machine-Learning für eingebettete Systeme.
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