Deep learning models are incredibly powerful but often tricky to adapt to new use cases. Whether you’re finetuning a pretrained net on new data, trying to build an intuition for a complex model, or throwing a variety of architectures at a unique problem, hyperparameter exploration can help. We will share high-level approaches and useful visualizations for hyperparameter search, grounded in concrete examples from semantic segmentation, image classification, language understanding, and other domains. Though we will focus on Weights & Biases Sweeps as a comprehensive tool for this task, these practices are framework-agnostic, and we hope they can accelerate your progress regardless of your dev setup.
