Practical AI Evaluating models without test data
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Sep 20, 2022 Charles Martin, an AI and data science consultant, created WeightWatcher, a unique tool for analyzing neural networks without needing test data. In this discussion, he dives into the challenges of evaluating machine learning models, especially in fields like retail and finance. He emphasizes the significance of human judgment and innovative methods for model assessment. Martin also highlights the integration of statistical methods from physics in AI, showcasing how they can enhance model training and optimization processes.
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Weight Watcher in Practice
- Daniel Whitenack analyzed his XLM Roberta-based question-answering model with Weight Watcher.
- The tool revealed 10 under-trained layers, prompting further investigation and adjustments.
Deep Learning and Multifractal Data
- Deep learning excels with natural data like text and images due to its multifractal structure.
- Neural networks effectively learn these multifractal patterns, which explains their success.
Troubleshooting Unconverged Layers
- Check regularization, dropouts, learning rates, and data sufficiency if layers haven't converged.
- Consider freezing earlier layers, extending training, or adjusting hyperparameters.