<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Deep-Learning on knowledged.to</title><link>https://knowledged.to/tags/deep-learning/</link><description>Recent content in Deep-Learning on knowledged.to</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 21 May 2026 21:20:38 +0530</lastBuildDate><atom:link href="https://knowledged.to/tags/deep-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Vectors vs Tensors</title><link>https://knowledged.to/notes/ml/vectors-vs-tensors/</link><pubDate>Thu, 21 May 2026 15:49:59 +0000</pubDate><guid>https://knowledged.to/notes/ml/vectors-vs-tensors/</guid><description>Explains how vectors relate to tensors in ML, including rank, framework terminology, and KV cache shapes.</description></item><item><title>Multi-Layer Perceptron (MLP)</title><link>https://knowledged.to/ai/concepts/multi-layer-perceptron/</link><pubDate>Sun, 17 May 2026 06:09:27 +0000</pubDate><guid>https://knowledged.to/ai/concepts/multi-layer-perceptron/</guid><description>Foundational neural network architecture covering perceptrons, layers, activation functions, and backpropagation-based training.</description></item><item><title>Attention in Machine Learning</title><link>https://knowledged.to/notes/ml/attention/</link><pubDate>Sun, 17 May 2026 05:54:45 +0000</pubDate><guid>https://knowledged.to/notes/ml/attention/</guid><description>Explanation of the attention mechanism in ML, covering Query/Key/Value, self-attention, multi-head, causal, cross-attention, and efficiency variants like FlashAttention and GQA.</description></item><item><title>Diffusion Models in AI</title><link>https://knowledged.to/notes/ml/diffusion-models/</link><pubDate>Fri, 17 Apr 2026 16:56:26 +0000</pubDate><guid>https://knowledged.to/notes/ml/diffusion-models/</guid><description>Overview of diffusion models, how they reverse a gradual noising process to generate data, key variants like DDPM, DDIM, and Latent Diffusion Models, and how text-to-image conditioning works</description></item></channel></rss>