Web Reference: Jul 23, 2025 · In this guide, we will discuss how you can employ the "Top 10 ML Debugging Techniques" which can help address and resolve issues even more promptly and effectively. This lecture provides a decision tree for debugging deep learning models and improving performance. This guide assumes that you already have an initial test dataset, a single metric to improve, and target performance based on human-level performance, published results, previous baselines, etc. Use a simple model to start out, it will make debugging your code a lot easier. If this model works, you can slowly start increasing its complexity (num layers, num neurons, weight decay etc)
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