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The Complete Guide to Transformer Neural Networks

CodeEmporium via YouTube

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Overview

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This course explains the Transformer neural network architecture for language translation, covering embeddings, positional encodings, query-key-value attention, residual connections, normalization, encoder-decoder attention, tokenization, and inference.

Syllabus

Introduction
Transformer at a high level
Why Batch Data? Why Fixed Length Sequence?
Embeddings
Positional Encodings
Query, Key and Value vectors
Masked Multi Head Self Attention
Residual Connections
Layer Normalization
Decoder
Masked Multi Head Cross Attention

Tokenization & Generating the next translated word
Transformer Inference Example

Taught by

CodeEmporium

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