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Table of Contents

• ↓ Clarifying exceptions and visualizing tensor operations in deep learning code (October 2020)
• ↓ Explaining RNNs without neural networks (July 2020)
• ↓ A visual explanation for regularization of linear models (May 2020)
• ↓ How to visualize decision trees (October 2018)
• ↓ How to explain gradient boosting (June 2018)
• ↓ The Matrix Calculus You Need For Deep Learning (February 2018)
• ↓ Top Alternatives to explained.ai

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"One of the biggest challenges when writing code to implement deep learning networks is getting all of the tensor (matrix and vector) dimensions to line up properly, even when using predefined network layers. This article describes a new library called TensorSensor that clarifies exceptions by augmenting messages and visualizing Python code to indicate the shape of tensor variables. It works with JAX, Tensorflow, PyTorch, and Numpy, as well as higher-level libraries like Keras and fastai. See also the TensorSensor implementation slides (PDF)."

"Vanilla recurrent neural networks (RNNs) form the basis of more sophisticated models, such as LSTMs and GRUs. But, sometimes the neural network metaphor makes it less clear exactly what's going on. This articles explains RNNs without neural networks, stripping them down to its essence—a series of vector transformations that result in embeddings for variable-length input vectors. I provide full PyTorch implementation notebooks that use just linear algebra and the autograd feature."

"Linear and logistic regression models are important because they are interpretable, fast, and form the basis of deep learning neural networks. Unfortunately, linear models have a tendency to chase outliers in the training data, which often leads to models that don't generalize well to new data. To produce models that generalize better, we all know to regularize our models. While there are lots of articles on the mechanics of regularized linear models, we've lack a simple and intuitive explanation for what exactly is going on during regularization. The goal of this article is to explain how regularization behaves visually, dispelling some myths and answering important questions along the way."

"(See video discussion.) 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 visualization packages are rudimentary and not immediately helpful to the novice. For example, we couldn't find a library that visualizes how decision nodes split up the feature space. So, we've created a general package called dtreeviz for scikit-learn decision tree visualization and model interpretation."

Key Topics & Focus Areas for explained.ai:

Clarifying exceptions and visualizing tensor operations in deep learning code (October 2020)

Explaining RNNs without neural networks (July 2020)

A visual explanation for regularization of linear models (May 2020)

How to visualize decision trees (October 2018)

How to explain gradient boosting (June 2018)

The Matrix Calculus You Need For Deep Learning (February 2018)