By leveraging advanced machine learning techniques like transfer learning, optimizing models with algorithms, and employing regularization to minimize overfitting, neural networks can be significantly improved. Furthermore, utilizing specialized hardware like GPUs/TPUs accelerates training and inference. The choice of ML frameworks (e.g., TensorFlow, PyTorch) impacts model development. Evaluating models using appropriate metrics ensures optimal performance, and understanding the relationship between ML and related fields provides a comprehensive understanding of this transformative technology.
