Dimensionality Reduction & Feature Extraction Techniques in Python: Part two
Part two of the Dimensionality Reduction & Feature Extraction goes over the real-world application of using these techniques. Download the file by clicking here
Requirements:ย
Anaconda Navigator / Jupyter notebook
Keras, ipywidgets, seaborn
How to use:
Compile the first 6 Cells
Stop on the cell starting with the code: display(test_size)
The last cell should generate a bunch of User Interfaces that can allow you to set the parameters you desire
Click run me to run the Deep Neural Network
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This Jupyter notebook will give you the chance to run the same Sequential Neural Network on the iris dataset with different reduction techniques, epochs, validation/verification split, and batch sizes. These parameters can affect the overall training time and accuracy of a neural network and I encourage you to find which set of parameters take the longest, and shortest training time!