ST-HardwareDes/MachLrngSyst

Study architectural techniques for efficient hardware design for machine learning (ML) systems including training and inference. Course has three parts. First part deals with ML algorithms: regression, support vector machines, decision tree, and naive Bayes approaches. Second part deals with convolutional and deep neural network models.
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