👉 Gains engineering, also known as model optimization or architecture search, is a set of techniques used in machine learning to improve the performance and efficiency of neural networks. It involves systematically modifying or searching for optimal configurations of a model's architecture, hyperparameters, or both, to achieve better predictive accuracy, faster training times, or reduced resource consumption. By exploring a wide range of possible designs, gains engineering can uncover novel architectures that outperform traditional ones, or it can fine-tune existing models to better fit specific tasks or datasets. This process often leverages automated tools and algorithms, such as reinforcement learning, evolutionary algorithms, or Bayesian optimization, to navigate the vast design space efficiently and effectively.
