Auto-PyTorch automates deep learning by performing neural architecture search and hyperparameter optimization jointly. It leverages SMAC as the underlying optimizer to find robust network configurations and training settings. The framework targets tabular data classification, regression, and time series forecasting tasks, allowing users to automatically discover effective neural network architectures without manual design.