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A summary and review of machine learning for image data, week 4: tips and tricks.

Well done for completing Week 4 of the course.

This week we looked at:

  • things to think about when training models, including:
    • dataset size – use of performance curves
    • splitting datasets – test and validation sets
    • cross-validation
  • data augmentation which, for images might include:
    • flipping
    • rotation
    • adding noise
  • common challenges and how to tackle them, including:
    • the curse of dimensionality and dimensionality reduction
    • overfitting and underfitting
    • class imbalance
    • regularisation, e.g. Lasso regression.

Next week, the final week, we will begin looking into deep learning, and how it differs from the machine learning techniques we have discussed so far.

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Machine Learning for Image Data

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