ML without tears

Category: Deep learning

  • A from-scratch implementation of Kolmogorov-Arnold Networks (KAN)…and MLP

    Kolmogorov-Arnold networks (KAN) are generating significant interest in the AI community due to their potential for accuracy and interpretability. We implement KAN (and MLPs, incidentally) from scratch in simple Python (no Torch / TensorFlow). The code is available on GitHub.

  • Backpropagation

    Chain rule to the rescue! To train a deep neural network (DNN), one needs to repeatedly compute the gradient of the empirical loss with respect to all the DNN parameters . Since their number can be huge, it is crucial that the procedure for gradient computation grows “slowly” with . Back-propagation addresses this successfully by exploiting shrewdly…