Training large-scale generative models presents several challenges, including high computational costs, memory limitations, and long training times. These models require massive datasets, making data preprocessing and quality control essential. Overfitting and mode collapse can also hinder performance, leading to less diverse outputs. Optimization strategies include using efficient model architectures, distributed computing, and mixed-precision training to reduce resource consumption. Techniques like regularization, adaptive learning rates, and gradient clipping help improve stability. Additionally, leveraging transfer learning and model pruning can enhance efficiency. Mastering these optimization methods is crucial for building powerful AI systems, making a generative AI course valuable for aspiring professionals.
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