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      <title>Continually Learning Blog</title>
      <link>https://continuallylearning.github.io</link>
      <description>Last 10 notes on Continually Learning Blog</description>
      <generator>Quartz -- quartz.jzhao.xyz</generator>
      <item>
    <title>Coulomb friction</title>
    <link>https://continuallylearning.github.io/Robotics/Coulomb-friction</link>
    <guid>https://continuallylearning.github.io/Robotics/Coulomb-friction</guid>
    <description><![CDATA[ Summary Notation \vec{F} denotes a vector, where F is the magnitude of vector \vec{F}. ]]></description>
    <pubDate>Thu, 04 Jun 2026 15:59:52 GMT</pubDate>
  </item><item>
    <title>Change of Variables for PDFs (Probability)</title>
    <link>https://continuallylearning.github.io/Math/Change-of-Variables-for-PDFs-(Probability)</link>
    <guid>https://continuallylearning.github.io/Math/Change-of-Variables-for-PDFs-(Probability)</guid>
    <description><![CDATA[ Summary Consider a random variable X that takes on real values in \mathbb{R}, and let f(x) be the probability density function (pdf) for the random variable X. ]]></description>
    <pubDate>Thu, 04 Jun 2026 14:15:56 GMT</pubDate>
  </item><item>
    <title>Gradients of random variables</title>
    <link>https://continuallylearning.github.io/Math/Gradients-of-random-variables</link>
    <guid>https://continuallylearning.github.io/Math/Gradients-of-random-variables</guid>
    <description><![CDATA[ Summary We will consider taking the gradients of three different expressions w.r.t \theta: \begin{align} L_1(\theta) &amp;= \mathbb{E}_{x \sim P}[F_\theta(x)] \\ L_2(\theta) &amp;= \mathbb{E}_{x \sim P_\theta}[F(x)] \\ L_3(\theta) &amp;= \mathbb{E}_{x \sim P_\theta}[F_\theta(x)] \\ \end{align} All t... ]]></description>
    <pubDate>Thu, 04 Jun 2026 14:15:56 GMT</pubDate>
  </item><item>
    <title>Behavior Cloning to Policy Gradients</title>
    <link>https://continuallylearning.github.io/Reinforcement-Learning/Behavior-Cloning-to-Policy-Gradients</link>
    <guid>https://continuallylearning.github.io/Reinforcement-Learning/Behavior-Cloning-to-Policy-Gradients</guid>
    <description><![CDATA[ Behavior cloning to score-based policy gradient This note makes the explicit connection between supervised learning (i.e., behavior cloning) and reinforcement learning (i.e., score-based policy gradients. ]]></description>
    <pubDate>Thu, 04 Jun 2026 14:15:56 GMT</pubDate>
  </item><item>
    <title>Sum of inner angles of polygon</title>
    <link>https://continuallylearning.github.io/Math/Sum-of-inner-angles-of-polygon</link>
    <guid>https://continuallylearning.github.io/Math/Sum-of-inner-angles-of-polygon</guid>
    <description><![CDATA[ Summary For an n-sided polygon, the sum of the inner angles is: \sum_{i=1}^{n} \theta_{i}^\text{inner} = 180(n-2) If n=3, then we see that the sum of inner angles if 180(3-2) = 180, which is a fact I memorized as a student. ]]></description>
    <pubDate>Wed, 03 Jun 2026 00:00:00 GMT</pubDate>
  </item><item>
    <title>Welcome to Continually Learning!</title>
    <link>https://continuallylearning.github.io/</link>
    <guid>https://continuallylearning.github.io/</guid>
    <description><![CDATA[ Hello, and welcome to my blog! These are blogs and notes I’ve (Eric Rosen) written to help me continually learn. ]]></description>
    <pubDate>Fri, 27 Mar 2026 18:07:53 GMT</pubDate>
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    <title>Gradient Theorem - Fundamental Theorem for Line Integrals</title>
    <link>https://continuallylearning.github.io/Math/Gradient-Theorem---Fundamental-Theorem-for-Line-Integrals</link>
    <guid>https://continuallylearning.github.io/Math/Gradient-Theorem---Fundamental-Theorem-for-Line-Integrals</guid>
    <description><![CDATA[ Prerequisites Fundamental Theorem of Calculus Line integral Gradient Theorem The gradient theorem, also known as the fundamental theorem of calculus for line integrals, says that the line integral of the gradient of a scalar field, \nabla f over a curve \gamma with start and end points a and b, is e... ]]></description>
    <pubDate>Sun, 22 Mar 2026 23:08:11 GMT</pubDate>
  </item><item>
    <title>Scalar Field and Vector Field</title>
    <link>https://continuallylearning.github.io/Math/Scalar-Field-and-Vector-Field</link>
    <guid>https://continuallylearning.github.io/Math/Scalar-Field-and-Vector-Field</guid>
    <description><![CDATA[ Vector Fields and Scalar Fields Summary Vector fields are functions that map vectors to other vectors. ]]></description>
    <pubDate>Sun, 22 Mar 2026 23:08:11 GMT</pubDate>
  </item><item>
    <title>Divergence Theorem</title>
    <link>https://continuallylearning.github.io/Math/Divergence-Theorem</link>
    <guid>https://continuallylearning.github.io/Math/Divergence-Theorem</guid>
    <description><![CDATA[ Prerequisites Divergence Divergence theorem (Fundamental Theorems for Divergences) Let \vec{F} be a vector field. ]]></description>
    <pubDate>Tue, 17 Feb 2026 22:26:01 GMT</pubDate>
  </item><item>
    <title>Fundamental Theorem of Calculus</title>
    <link>https://continuallylearning.github.io/Math/Fundamental-Theorem-of-Calculus</link>
    <guid>https://continuallylearning.github.io/Math/Fundamental-Theorem-of-Calculus</guid>
    <description><![CDATA[ Fundamental Theorem of Calculus Let f(x) be a function of one variable. ]]></description>
    <pubDate>Tue, 17 Feb 2026 22:26:01 GMT</pubDate>
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