A complete explanation of all the layers of a Transformer Model: MultiHead SelfAttention, Positional Encoding, including all the matrix multiplications and a complete description of the training and inference process.

Paper: Attention is all you need https://arxiv.org/abs/1706.03762

Slides PDF: https://github.com/hkproj/transformer...

Chapters
00:00 Intro
01:10 RNN and their problems
08:04 Transformer Model
09:02 Maths background and notations
12:20 Encoder (overview)
12:31 Input Embeddings
15:04 Positional Encoding
20:08 Single Head SelfAttention
28:30 MultiHead Attention
35:39 Query, Key, Value
37:55 Layer Normalization
40:13 Decoder (overview)
42:24 Masked MultiHead Attention
44:59 Training
52:09 Inference