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You are here		mattkeeter.com Gradients are the new intervals
\|	\|	blog.evjang.com Eric Jang: Differentiable Path Tracing on the GPU/TPU	5.0 parsecs away Travel
\|	\|	You can download a PDF (typset in LaTeX) of this blog post here . Jupyter Notebook Code on GitHub: https://github.com/ericjang/pt-jax ...	5.0 parsecs away Travel
\|	\|	blog.demofox.org Derivatives, Gradients, Jacobians and Hessians - Oh My! « The blog at the bottom of the sea	4.3 parsecs away Travel
\|	\|	This article explains how these four things fit together and shows some examples of what they are used for. Derivatives Derivatives are the most fundamental concept in calculus. If you have a function, a derivative tells you how much that function changes at each point. If we start with the function $latex y=x^2-6x+13$, we can...	4.3 parsecs away Travel
\|	\|	jingnanshi.com Automatic Differentiation: Forward and Reverse	3.3 parsecs away Travel
\|	\|	Tutorial on automatic differentiation	3.3 parsecs away Travel
\|	\|	www.jeremykun.com Duality for the SVM \| Math Programming	28.3 parsecs away Travel
\|		This post is a sequel to Formulating the Support Vector Machine Optimization Problem. The Karush-Kuhn-Tucker theorem Generic optimization problems are hard to solve efficiently. However, optimization problems whose objective and constraints have special structure often succumb to analytic simplifications. For example, if you want to optimize a linear function subject to linear equality constraints, one can compute the Lagrangian of the system and find the zeros of its gradient. More generally, optimizing...	28.3 parsecs away Travel