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You are here		antoinevastel.com A simple SVD recommender system using Python
\|	\|	fa.bianp.net How to Evaluate the Logistic Loss and not NaN trying	5.0 parsecs away Travel
\|	\|	A naive implementation of the logistic regression loss can results in numerical indeterminacy even for moderate values. This post takes a closer look into the source of these instabilities and discusses more robust Python implementations. hljs.initHighlightingOnLoad(); MathJax.Hub.Config({ extensions: ["tex2jax.js"], jax: ["input/TeX", "output/HTML-CSS"], tex2jax: { inlineMath ...	5.0 parsecs away Travel
\|	\|	yoursite.com ??Numpy??????[??] \| Yin's Blog	5.8 parsecs away Travel
\|	\|	??????NumPy??????????????Jay Mody????????????????????????????????????NumPy????????????? ?????Computing Distance Matrices with NumPy ?????? (????????)	5.8 parsecs away Travel
\|	\|	blog.georgeshakan.com Singular Value Decomposition and PCA \| George Shakan	4.1 parsecs away Travel
\|	\|	Principal Component Analysis (PCA) is a popular technique in machine learning for dimension reduction. It can be derived from Singular Value Decomposition (SVD) which we will discuss in this post. We will cover the math, an example in python, and finally some intuition. The Math SVD asserts that any $latex m \times d$ matrix $latex...	4.1 parsecs away Travel
\|	\|	distill.pub Exploring Bayesian Optimization	24.2 parsecs away Travel
\|		How to tune hyperparameters for your machine learning model using Bayesian optimization.	24.2 parsecs away Travel