<?xml version="1.0" encoding="UTF-8" ?>
<rss version="2.0">
    <channel>
      <title>Anish&#039;s Notes</title>
      <link>https://anishkamatam.github.io/anishsnotes</link>
      <description>Last 10 notes on Anish&#039;s Notes</description>
      <generator>Quartz -- quartz.jzhao.xyz</generator>
      <item>
    <title>Transformers Explained</title>
    <link>https://anishkamatam.github.io/anishsnotes/Transformers-Explained</link>
    <guid>https://anishkamatam.github.io/anishsnotes/Transformers-Explained</guid>
    <description><![CDATA[ What is a Transformer? A Transformer is a neural network architecture that has fundamentally reshaped modern artificial intelligence. ]]></description>
    <pubDate>Fri, 30 Jan 2026 00:28:16 GMT</pubDate>
  </item><item>
    <title>Fundamentals of GPU Architectures</title>
    <link>https://anishkamatam.github.io/anishsnotes/Fundamentals-of-GPU-Architectures</link>
    <guid>https://anishkamatam.github.io/anishsnotes/Fundamentals-of-GPU-Architectures</guid>
    <description><![CDATA[ At the highest level, a GPU performs two essential tasks: Move and store data (the memory system) Do useful work with the data (the compute pipelines) The block diagram of H100 below reflects this division: components in blue represent memory or data movement, while components in red are compute (ho... ]]></description>
    <pubDate>Thu, 29 Jan 2026 23:13:39 GMT</pubDate>
  </item><item>
    <title>Scaling LLMs Across GPU Clusters</title>
    <link>https://anishkamatam.github.io/anishsnotes/Scaling-LLMs-Across-GPU-Clusters</link>
    <guid>https://anishkamatam.github.io/anishsnotes/Scaling-LLMs-Across-GPU-Clusters</guid>
    <description><![CDATA[ Model Training can be derived into 3 steps: Forward Pass - inputs through model to yield outputs Backward Pass - compute gradients Optimization Step - update parameters via gradient descent The batch size (bs) is one of the important hyperparameters for model training; it affects both model converge... ]]></description>
    <pubDate>Thu, 29 Jan 2026 22:42:47 GMT</pubDate>
  </item><item>
    <title>Welcome to Anish&#039;s Notes</title>
    <link>https://anishkamatam.github.io/anishsnotes/</link>
    <guid>https://anishkamatam.github.io/anishsnotes/</guid>
    <description><![CDATA[ Welcome to Anish’s Notes Hi, I’m Anish. I’m a CS + Math student who loves working on systems, AI, and robotics. ]]></description>
    <pubDate>Thu, 04 Dec 2025 01:28:38 GMT</pubDate>
  </item>
    </channel>
  </rss>