This document discusses implementing machine learning at the IoT edge using neural networks like GRU. It covers neural network types and cells, training tools, numeric formats for embedded implementations, and references for CMSIS-NN, HDF5, TensorFlow, Python and more. Code examples demonstrate converting models to Q format and optimizing matrix storage for efficient embedded inference.