Visual Debugging Tools for Machine Learning Workflows
Visual debugging tools are essential for understanding machine learning model training. They help identify issues such as overfitting and vanishing gradients by visualizing gradients, losses, and embeddings. Tools like TensorBoard can provide insights that improve model performance during training.
- ▪Visual debugging tools enhance the understanding of machine learning model training.
- ▪Loss curves are crucial for identifying overfitting and learning issues.
- ▪Gradient visualization helps detect the vanishing gradient problem in deep networks.
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# Introduction Training a machine learning model and observing the loss decrease is a feeling of progress, until the validation accuracy reaches a plateau or the loss begins to spike, and you're not sure what caused it. At that point, most people add more logging or start tuning hyperparameters, hoping something changes. What most analysts skip at this stage is actual visibility into what is happening inside the model during training. Visual debugging tools can provide useful insights at this stage. In this article, we cover three topics: what to visualize during training (gradients, losses, and embeddings), the tools that provide those visualizations (TensorBoard and its main alternatives), and the methods to capture model computations directly using hooks and breakpoints.
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