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Master advanced ML/DS statistics
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Module 12
Modern Sequence Modeling with Transformers
Encoder-Decoder Architecture for Sequence-to-Sequence Models
Describe the encoder-decoder architecture for sequence-to-sequence tasks like machine translation.
2
The Fixed-Size Bottleneck
Explain the limitation of the fixed-size context vector in traditional encoder-decoder models.
3
Understanding Attention: Focusing on Relevant Inputs
Explain the concept of attention as a mechanism to dynamically focus on relevant input parts.
4
Calculating Attention Scores
Calculate attention scores using the query, key, and value formulation.
5
Understanding Scaled Dot-Product Attention
Describe how scaled dot-product attention works.
6
Multi-Head Attention: Unpacking Representation Subspaces
Explain the purpose of multi-head attention in capturing different representation subspaces.
7
Positional Encodings: Understanding Sequence Order
Describe the role of positional encodings in providing sequence order information to the model.
8
Transformer Architecture: A Comprehensive Overview
Outline the complete architecture of a Transformer, combining self-attention, feed-forward layers, and residual connections.
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