Vizuara Books
Transformers: Theory, intuition, and Building from Scratch
The transformer, from intuition to code.
Attention, positional encoding, and the full transformer architecture built from first principles and illustrated.
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00Introduction and Motivation2 capsules
Introduction and Motivation — 2 chapters.
01Example/Motivationfree13 min02Course Introductionconcept🔒12 min01Foundations of Attention2 capsules
Foundations of Attention — 2 chapters.
03Understanding Attention🔒14 min04Attention Example🔒14 min02Challenges with Traditional Models1 capsules
Challenges with Traditional Models — 1 chapters.
05Shortcomings of Sequence-to-Sequence Models🔒14 min03Introduction to Transformers2 capsules
Introduction to Transformers — 2 chapters.
06Word Embeddings🔒13 min07Transformer Model Introconcept🔒14 min04Positional Encoding4 capsules
Positional Encoding — 4 chapters.
08Why Do We Need Positional Encoding?🔒14 min09Approaching Positional Encoding🔒14 min10Sinusoidal Encoding🔒13 min11Other Positional Encodings🔒14 min05Attention Mechanisms in Transformers3 capsules
Attention Mechanisms in Transformers — 3 chapters.
12Intro to Attention in Transformersconcept🔒14 min13Scaled Dot-Product Attentionmath🔒14 min14Multi-Head Attention🔒14 min06Piecing It All Together2 capsules
Piecing It All Together.
15The Encoder Blockconcept🔒13 min16The Decoder and Final Outputconcept🔒13 min07Implementation & Code Walkthrough3 capsules
Implementation & Code Walkthrough.
17Tokenization and the Datasetcode🔒14 min18Coding the Input and Attention Layerscode🔒13 min19Assembling and Training the Transformercode🔒14 min08Extensions and Advanced Topics1 capsules
Extensions and Advanced Topics.
20Other Transformer-Based Architecturesconcept🔒12 minRatings & reviews
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