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Most of us last saw calculus in school, but derivatives are a critical part of machine learning, particularly deep neural networks, which are trained by optimizing a loss function. This article is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We…
Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. Unfortunately, current…
A deep dive into exactly how regularization works, using visualizations rather than (just) mathematics. This article also dispels some myths and answer some important questions about Lasso and Ridge regression.
This article explains how recurrent neural networks (RNN's) work without using the neural network metaphor. It uses a visually-focused data-transformation perspective to show how RNNs encode variable-length input vectors as fixed-length embeddings. Included are PyTorch implementation notebooks that use just linear…