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<h2 class="heading-element" dir="auto">Byte Pair Encoding in LLMs</h2>
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<p dir="auto">In this blog, we will learn about BPE (Byte Pair Encoding) - the tokenization algorithm used by most modern Large Language Models (LLMs) to break text into smaller pieces before processing it.</p>
<p dir="auto">We will understand what BPE is, why it is needed, and how it works step by step with a simple example.</p>
<p dir="auto">We will cover the following:</p>
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<li>What is Tokenization?</li>
<li>The Problem: How to Break Text into Tokens?</li>
<li>What is BPE (Byte Pair Encoding)?</li>
<li>How BPE Works: Step by Step</li>
<li>How BPE Tokenizes New Text</li>
<li>Why BPE is Used in Modern LLMs</li>
</ul>
<p dir="auto">Let's get started: <a href="https://outcomeschool.com/blog/bpe-in-llms" rel="nofollow">Byte Pair Encoding in LLMs</a>&nbsp;</p>
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<h2 class="heading-element" dir="auto">Math behind Attention - Q, K, and V</h2>
In this blog, we will learn about the math behind Attention: Query(Q), Key(K), and Value(V) with a step-by-step numeric example.</div>
<p dir="auto">We will cover the following:</p>
<ul dir="auto">
<li>The Attention Formula</li>
<li>Setting Up: From Words to Vectors</li>
<li>Creating Q, K, and V Matrices</li>
<li>Computing Attention Scores (Q x K^T)</li>
<li>Scaling the Scores</li>
<li>Applying Softmax</li>
<li>Computing the Final Output (Attention Weights x V)</li>
<li>Putting It All Together</li>
</ul>
<p dir="auto">Let's get started: <a href="https://outcomeschool.com/blog/math-behind-attention-qkv" rel="nofollow">Math behind Attention - Q, K, and V</a></p>
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Lista tag dell'oggetto (oggettag)
cod_tag	value
deep learning	5
language_model	5
linear_regression	5
machine learning	5
model_training	5
wpg	5

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