Automated Machine Learning (AutoML) effectively addresses complex tasks such as Neural Architecture Search and Hyperparameter Optimization through methodologies like sequential model-based optimization and genetic programming.

Title: P0: AutoML ⤜(⚆i⚆)⤏ [2023-12-06 Wed] Major papers: - Automated Machine Learning - Methods, Systems, Challenges. Springer, 2019 https://www.automl.org/wp-content/uploads/2019/05/AutoML_Book.pdf - sequential model-based optimization (Hutter et al., 2011 Snoek et al., 2012), - hierarchical task planning (Erol et al., 1994) - genetic programming (Koza, 1992) tasks that AutoML solve: - Neural Architecture Search (NAS) - Hyperparameter Optimization\n#automl #ml #nn

Title: P0: AutoML ⤜(⚆i⚆)⤏ [2023-12-06 Wed] Major papers: - Automated Machine Learning - Methods, Systems, Challenges. Springer, 2019 https://www.automl.org/wp-content/uploads/2019/05/AutoML_Book.pdf - sequential model-based optimization (Hutter et al., 2011 Snoek et al., 2012), - hierarchical task planning (Erol et al., 1994) - genetic programming (Koza, 1992) tasks that AutoML solve: - Neural Architecture Search (NAS) - Hyperparameter Optimization\n#automl #ml #nn