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Deep Learning / 17

Transformers

Connect attention to Transformer blocks, embeddings, position, training, and inference.

Section 17 of 32 · 21 subtopics

Section / Insight

Attention changed how information moves. Transformers changed how models scale that movement.

This is a knowledge index. Published titles open their articles; the remaining entries outline planned coverage.

Research Papers & Insights / Paper → Product

Attention changed how information moves.

Understand the Transformer’s central idea, what the original experiments reported, what they did not establish, and why the architecture still matters to engineers.

Subtopics

  1. 17.01

    Why Transformers?

    Planned guide: Why Transformers. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  2. 17.02

    Transformer Architecture

    Planned guide: Transformer Architecture. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  3. 17.03

    Token Embeddings

    Planned guide: Token Embeddings. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  4. 17.04

    Positional Information

    Planned guide: Positional Information. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  5. 17.05

    Positional Encoding

    Planned guide: Positional Encoding. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  6. 17.06

    Self-Attention Layer

    Planned guide: Self-Attention Layer. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  7. 17.07

    Feedforward Network

    Planned guide: Feedforward Network. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  8. 17.08

    Residual Connections

    Planned guide: Residual Connections. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  9. 17.09

    Layer Normalization

    Planned guide: Layer Normalization. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  10. 17.10

    Encoder

    Planned guide: Encoder. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  11. 17.11

    Decoder

    Planned guide: Decoder. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  12. 17.12

    Encoder-Decoder Transformer

    Planned guide: Encoder-Decoder Transformer. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  13. 17.13

    Decoder-Only Transformer

    Planned guide: Decoder-Only Transformer. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  14. 17.14

    Encoder-Only Transformer

    Planned guide: Encoder-Only Transformer. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  15. 17.15

    Causal Masking

    Planned guide: Causal Masking. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  16. 17.16

    Multi-Head Attention

    Planned guide: Multi-Head Attention. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  17. 17.17

    Transformer Block

    Planned guide: Transformer Block. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  18. 17.18

    Context Length

    Planned guide: Context Length. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  19. 17.19

    KV Cache

    Planned guide: KV Cache. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  20. 17.20

    Transformer Training

    Planned guide: Transformer Training. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation
  21. 17.21

    Transformer Inference

    Planned guide: Transformer Inference. Focus on intuition, assumptions, and a worked example.

    intermediateArticle in preparation