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Implementing a 2-class Classification Neural Network with a Single Hidden Layer
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The Model Comprises of:

Visualising the dataset using matplotlib. The data looks like a "flower" with some red (label y=0) and some blue (y=1) points. Number of training examples is 400. The goal is to build a model to fit this data. The Dataset contains a numpy-array (matrix) X that contains the features (x1, x2) and a numpy-array (vector) Y that contains your labels (red:0, blue:1). The size of hidden layer is 4.

Result :

  1. The Neural Network model with one hidden layer has learnt the leaf patterns of the flower!

  2. Accuracy is 90%

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