Let's deep dive into the transformer encoder architecture.
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[ 2 🔎] Transformer Main Paper: https://arxiv.org/abs/1706.03762
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TIMESTAMPS
0:00 Introduction
0:28 Encoder Overview
1:25 Blowing up the encoder
1:45 Create Initial Embeddings
3:54 Positional Encodings
4:54 The Encoder Layer Begins
5:02 Query, Key, Value Vectors
7:37 Constructing Self Attention Matrix
9:44 Why scaling and Softmax?
10:53 Combining Attention heads
12:46 Residual Connections (Skip Connections)
13:45 Layer Normalization
16:36 Why Linear Layers, ReLU, Dropout
17:46 Complete the Encoder Layer
18:46 Final Word Embeddings
20:04 Sneak Peak of Code