thomasj02/DeepLearningProjectWorkflow
Machine Learning Workflow, from Andrew Ng's lecture at Deep Learning Summer School 2016
This document outlines a structured approach for improving the performance of a deep learning model. It guides you through analyzing model errors—like the difference between human-level and model performance—to identify whether your model is underfitting or overfitting. This helps machine learning practitioners efficiently diagnose and address common issues in their deep learning projects.
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Use this if you are a machine learning engineer or data scientist struggling to improve your model's accuracy and need a systematic way to debug its performance.
Not ideal if you are new to machine learning and haven't yet built a basic model, as it assumes familiarity with concepts like training and test sets.
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