Machine Learning for Embedded Systems with ARM Ethos-U NPU

Partner: Udemy
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Description: Machine Learning for Embedded Systems with ARM Ethos-UAre you ready to bring the power of machine learning into the world of embedded systems?This course takes you on a complete, hands-on journey from building and training models to running them on real ARM-based hardware with dedicated NPUs.Most ML courses stop at theory or training. This one goes further: you’ll actually deploy and run models on embedded devices, bridging the gap between machine learning and practical engineering.What you’ll learnThe core ML theory behind embedded AIUnderstand the stages of a neural network execution pipelineExplore convolution, flattening, activation functions, and softmax in CNNsLearn how ML operations are optimized for resource-constrained devicesModel preparation workflowTrain models in TensorFlowConvert them into lightweight .tflite modelsOptimize and compile with the ARM Vela compiler for the Ethos-U NPURunning inference on embedded devicesExecute models with TensorFlow Lite Micro (TFLM) in C++See how ML operations map to CMSIS-NN kernels and the Ethos-U hardware acceleratorUnderstand the complete inference path — from model to siliconHands-on with real hardwareSet up and run the Alif E7 ML Development KitBuild and deploy Keyword Spotting and Image Classification demosObserve real-time outputs directly on the deviceWhy this course is uniqueBridges the gap between ML theory and real embedded deploymentCovers the entire workflow — from training to NPU executionPractical, hardware-driven approach using the Alif E7 ML dev kit</
Category: IT & Software > Other IT & Software > Machine Learning
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Price: 94.99
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Source: Impact
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