Fine-Tuning Framework
Itamar discusses the mathematical approach to fine-tuning neural networks, emphasizing the importance of understanding the geometry of data representation. By analyzing activations and partitions, one can determine which aspects of the model to retain or adjust. Additionally, the best practice involves using real data to generate synthetic datasets, enhancing the model's training and evaluation process.In this clip
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