- RBF Networks for Function Approximation
Implementing a Radial Basis Function network with K-Means clustering and LMS training to approximate a noisy sinusoidal function.
6 min - Backpropagation and the N-Bit Parity Problem
Implementing a multi-layer perceptron from scratch to solve the N-bit parity problem, and analyzing the effects of learning rate and momentum.
5 min - Perceptron Learning, the XOR Problem, and Gradient Descent
Applying the perceptron learning rule, confronting the XOR limitation, designing multi-layer networks, and connecting gradient descent to LMS.
5 min - McCulloch-Pitts Neurons and Linear Separability
Designing M-P neurons for conditional logic, building binary adders from neural networks, and exploring the limits of linear separability.
4 min
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