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1
Introduction
2
Encoder Overview
3
Blowing up the encoder
4
Create Initial Embeddings
5
Positional Encodings
6
The Encoder Layer Begins
7
Query, Key, Value Vectors
8
Constructing Self Attention Matrix
9
Why scaling and Softmax?
10
Combining Attention heads
11
Residual Connections Skip Connections
12
Layer Normalization
13
Why Linear Layers, ReLU, Dropout
14
Complete the Encoder Layer
15
Final Word Embeddings
16
Sneak Peak of Code
Description:
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.

Deep Dive into the Transformer Encoder Architecture

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#Computer Science #Deep Learning #Transformer Architecture #Artificial Intelligence #Neural Networks #Machine Learning #Embeddings #Self-Attention #Positional Encoding
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Introduction