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1 Deep learning Classification

  • Download the SOCR Alzheimer’s disease data
  • Preprocess the data and pool the MCI and AD cohorts (patients)
  • Build a multi-layer perceptron as a classifier (patients vs. controls) and select proper parameters
  • Classify AD and NC and report detailed evaluations, including cross table, accuracy, sensitivity, specificity, LOR, AUC
  • Provide some visualizations, e.g., histogram and model structure graph as we did in Chapter 22
  • Then, try to perform a multi-classes modeling (i.e., AD, NC and MCI) and report the classification results.

2 Deep learning Regression

  • Download the Allometric relationship data from the SOCR data archive
  • Preprocess the data and set density as outcome response feature
  • Create a MXNet feed-forward neural net model and properly specify the parameters
  • Train and predict the density using this model and report RMSE on the test data, evaluate the result and justify your evaluation
  • Output the model structure.

3 Image classification

Apply the deep learning neural network techniques to classify some images with pre-trained model as we did in Chapter 22:

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