Deep Dive into the Transformer Encoder Architecture

Dive deep into the transformer encoder architecture in this 21-minute video tutorial. Explore the intricacies of initial embeddings, positional encodings, and the encoder layer structure. Learn about query, key, and value vectors, self-attention matrix construction, and the importance of scaling and softmax. Understand the combination of attention heads, residual connections, layer normalization, and the role of linear layers, ReLU, and dropout. Conclude with insights on final word embeddings and a sneak peek at the code implementation.

Introduction
Encoder Overview
Blowing up the encoder
Create Initial Embeddings
Positional Encodings
The Encoder Layer Begins
Query, Key, Value Vectors
Constructing Self Attention Matrix
Why scaling and Softmax?
Combining Attention heads
Residual Connections Skip Connections
Layer Normalization
Why Linear Layers, ReLU, Dropout
Complete the Encoder Layer
Final Word Embeddings
Sneak Peak of Code