Deep learning problem

Please analyze the problem and explain it first

The linearly nonseparable patterns X1,X2,,X10 listed below must be classified into two categories using a two-layer network. Class 1: X4={2,2}t,X6={2,1.5}t,X7={2,0}t,X8={1,0}t, and X9={3,0}t Class 2: X1={1,3}t,X2={3,3}t,X3={1,2}t,X5={3,2}t, and X10={5,0}t Design a two-layer network with bipolar discrete neurons using the parametric approach to classify the above ten patterns. - The weights of the first layer should be determined such that the outputs of the first layer are linearly separable. - Provide a plot of the input patterns with the chosen hypersurfaces. Page 2 of 2 - Determine the hypersurface equations in the input pattern space that transforms the linearly nonseparable patterns into two class linearly separable patterns in the transformed pattern space. The number of hypersurface equations dictates the number of neurons in the first layer. - Determine the weights of the single neuron in the second layer to classify the linearly separable patterns in the transformed pattern space. - Demonstrate the performance of the designed network using the input patterns