ML Fundamentals for Hardware Engineers
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Bridge the gap between machine learning and hardware engineering. Covers ML fundamentals (linear models, neural networks, CNNs, transformers), training vs inference, quantisation, pruning, and how ML workloads map to hardware. Designed for VLSI, FPGA, and embedded engineers who need AI/ML literacy f
