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The Role of Simulations in Neural Network Training
This chapter examines the crucial role that synthetic data and game engines play in the development of neural network models. It highlights how simulations can create efficient learning environments, enabling neural nets to adopt behaviors with fewer examples through mechanisms like few-shot learning. The discussion also draws parallels between human cognitive processes and machine learning, suggesting innovative approaches for integrating long-term memory into neural architectures.