Learn about the impact of hyperparameter sensitivity in deep neural network pruning through this technical conference talk from tinyML EMEA. Explore how structured pruning methods affect model deployment on resource-constrained devices, with a focus on reducing inference latency, memory footprint, and energy consumption. Discover state-of-the-art hyperparameter optimization techniques, including Bayesian optimization and BOHB, and understand their application in training various models on public datasets. Follow along as the speaker demonstrates how to analyze hyperparameter-performance distributions before and after pruning, quantifying sensitivity through distance metrics. Gain valuable insights into maximizing network performance on resource-limited hardware and deepen your understanding of neural network generalization principles through practical examples and experimental results.