<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Adam Lineberry</title><description>Notes and writing on machine learning.</description><link>https://adamlineberry.io/</link><item><title>Getting Up to Speed with SHAP for Model Interpretability</title><link>https://adamlineberry.io/shap/</link><guid isPermaLink="true">https://adamlineberry.io/shap/</guid><description>Value proposition, intuition, and analytical breakdown of SHAP</description><pubDate>Fri, 17 Jan 2020 00:00:00 GMT</pubDate></item><item><title>Logistic Regression Deep Dive</title><link>https://adamlineberry.io/logistic-regression/</link><guid isPermaLink="true">https://adamlineberry.io/logistic-regression/</guid><description>Derivation of objective and gradient, visualizing a gradient update</description><pubDate>Thu, 14 Nov 2019 00:00:00 GMT</pubDate></item><item><title>A Quick Primer on KL Divergence</title><link>https://adamlineberry.io/vae-series/kl-divergence/</link><guid isPermaLink="true">https://adamlineberry.io/vae-series/kl-divergence/</guid><description>Intuitive introduction to KL divergence, including discussion on its asymmetry</description><pubDate>Sun, 07 Jul 2019 00:00:00 GMT</pubDate></item><item><title>Latent Variable Models, Expectation Maximization, and Variational Inference</title><link>https://adamlineberry.io/vae-series/variational-inference/</link><guid isPermaLink="true">https://adamlineberry.io/vae-series/variational-inference/</guid><description>Introduction to latent variable models, derivation of the ELBO, and the relationship with Expectation Maximization</description><pubDate>Sun, 07 Jul 2019 00:00:00 GMT</pubDate></item><item><title>Variational Autoencoder Theory</title><link>https://adamlineberry.io/vae-series/vae-theory/</link><guid isPermaLink="true">https://adamlineberry.io/vae-series/vae-theory/</guid><description>Formulation of the VAE objective, neural network architecture design, optimization, and practical uses</description><pubDate>Sun, 07 Jul 2019 00:00:00 GMT</pubDate></item><item><title>Blog Post Series: From KL Divergence to Variational Autoencoder in PyTorch</title><link>https://adamlineberry.io/vae-series/</link><guid isPermaLink="true">https://adamlineberry.io/vae-series/</guid><description>Landing page for the blog post series</description><pubDate>Sun, 07 Jul 2019 00:00:00 GMT</pubDate></item><item><title>Variational Autoencoder Code and Experiments</title><link>https://adamlineberry.io/vae-series/vae-code-experiments/</link><guid isPermaLink="true">https://adamlineberry.io/vae-series/vae-code-experiments/</guid><description>Detailed walkthrough of a PyTorch VAE implementation trained on MNIST, including visualizations of data generation, reconstruction, and anomaly detection</description><pubDate>Sun, 07 Jul 2019 00:00:00 GMT</pubDate></item><item><title>Interesting Details about ROC Curve Calculations</title><link>https://adamlineberry.io/how-auroc-is-calculated/</link><guid isPermaLink="true">https://adamlineberry.io/how-auroc-is-calculated/</guid><description>Code-based walkthrough showing how AUC is computed for a simple model trained on a simple dataset</description><pubDate>Mon, 20 May 2019 00:00:00 GMT</pubDate></item></channel></rss>