SKU/Artículo: AMZ-B0G4DZVX4F

Embedded TinyML: A Hands-on Guide to Deploying Intelligent and Advanced AI on Resource-Constrained Microcontrollers

Disponibilidad:
Fuera de stock
Peso con empaque:
0.97 kg
Devolución:
No
Condición
Nuevo
Producto de:
Amazon

Sobre este producto
  • The Philosophy of Constraints: How to turn memory limits (kB) and clock speeds (MHz) into drivers for efficient engineering.
  • The Hardware Stack: Deep dives into ARM Cortex-M architecture, DSPs, and NPUs.
  • Energy Profiling: Master power management strategies to measure and minimize consumption per inference.
  • Model Optimization: A complete breakdown of Quantization (Int8 vs Float32), Pruning, and Architecture Search.
  • TensorFlow Lite Micro: Navigate the TFLite ecosystem, from training in Keras/Python to C++ deployment.
  • 1. Proprioception: Build a multi-class gesture recognition wand using IMU sensor fusion.
  • 2. Vision: Create a privacy-preserving "Visual Wake-Word" detector on low-res camera modules.
  • 3. Industrial IoT: Develop an unsupervised Anomaly Detection system for predictive maintenance on vibrating machinery.
  • 4. Voice Interface: Engineer a two-stage keyword spotting pipeline for voice control.

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