With a TensorFlow™ Lite neural network, code generation using either the STM32Cube. Easy portability across different STM32 microcontroller series through STM32Cube integration. Possibility to use larger networks by storing weights in external flash memory and activation buffers in external RAM. Relocatable option enabling standalone model update during product lifecycle by creating a model binary code separated from the application code. 
Features a built-in breaching tool pocket and two side access mag pockets. Streamlined no-snag design is not too big and not too small. Several handy internal and external pockets.
Support for deeply quantized neural networks (down to 1-bit) from QKeras and Larq Pack Zip-On Panel 2.0 Sizing Chart Description Simple, versatile pack that zips to rear platebag and stretches to hold essential supplies. Support for the 8-bit quantization of Keras networks and TensorFlow™ Lite quantized networks. Support for various built-in scikit-learn models such as isolation forest, support vector machine (SVM), K-means, and more. Native support for various deep learning frameworks such as Keras and TensorFlow™ Lite, and support for all frameworks that can export to the ONNX standard format such as PyTorch™, MATLAB ®, and more. Generation of an STM32-optimized library from pretrained neural network and classical machine learning models Posts: 5 Karma: 1 Le & Spark Expansion packs « on: May 14, 2013, 07:03:10 pm » Can I use Spark Expansion packs with SparkLE - the new Hollywood pack in particular The spec details dont include LE in them I would be very greatful if someone could help, before I purchase.
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