BP Neural Network Curve Fitting Without Toolbox Implementation

In this project, we implement BP neural network for curve fitting without using any pre-built toolbox functions. The instructor prohibited toolbox usage, requiring manual coding to deepen our understanding of neural network fundamentals. This approach involves implementing core components including forward propagation with activation functions (typically sigmoid/tanh for hidden layers), error calculation using mean squared error, and backpropagation with gradient descent optimization. Key implementation steps include: 1) Network architecture initialization with randomized weights between layers, 2) Forward pass computation through weighted sums and activation functions, 3) Error backpropagation using chain rule derivatives to update weights incrementally. Though more time-consuming, this hands-on coding process provides invaluable insights into neural network mechanics and strengthens foundational knowledge. Let's begin coding the matrix-based operations for neuron connections and iterative training cycles!