Inspired by the Kolmogorov-Arnold representation theorem, this project applies it to the design of neural networks. The main difference between KAN (Kolmogorov-Arnold Network) and the traditional Multilayer Perceptron (MLP) architecture is the application of activation functions. In KAN, activation functions are placed on the weights, resulting in a potentially more accurate and interpretable, albeit sometimes slower to train, network than the MLP.