<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Training Strategy on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/training-strategy/</link><description>Recent content in Training Strategy on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/training-strategy/index.xml" rel="self" type="application/rss+xml"/><item><title>Curriculum Learning</title><link>https://terms-en.ai-term-hub.com/en/terms/curriculum_learning/</link><pubDate>Sat, 18 Jul 2026 10:20:32 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/curriculum_learning/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Curriculum learning mimics human education by presenting training data in a structured order, typically starting with simple samples and gradually increasing complexity. This approach helps neural networks converge faster, avoid local minima, and achieve better generalization performance compared to random data shuffling. It requires defining a meaningful difficulty metric for the dataset to sequence samples effectively during the training process.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>A training strategy where models learn from easy examples first before progressing to harder ones.&lt;/p></description></item><item><title>Zero-shot Learning</title><link>https://terms-en.ai-term-hub.com/en/terms/zero_shot_learning/</link><pubDate>Sat, 18 Jul 2026 09:43:55 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/zero_shot_learning/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Zero-shot learning enables a machine learning model to classify instances of classes that were not present in its training dataset. Instead of relying on labeled examples for every possible class, the model uses auxiliary information, such as textual descriptions or attribute vectors, to infer relationships between known and unknown classes. This approach significantly reduces the need for extensive labeled data and allows models to generalize to new concepts based on learned semantic structures.&lt;/p></description></item></channel></rss>