Learning Path

Attention-Based Architectures and Transformers

Deep Learning Fundamentals → Implementing a Transformer from Scratch

13 Knowledge Nodes · 13 Learning Steps · v0 Active

A comprehensive graduate-level learning path covering attention mechanisms, transformer architectures, and their applications in NLP and vision. Starting from foundational deep learning concepts, the path progresses through self-attention, multi-head attention, the transformer encoder-decoder, and major variants such as BERT, GPT, and vision transformers, with practical implementation exercises.

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13 nodes18 edges2 relationships

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Learning Route

13 learning steps · 3 phases. Click any step to inspect it and see it on the Knowledge Map.

Phase 1

Foundation

5 steps

Phase 2

Core Concepts

4 steps

Phase 3

Advanced Topics

4 steps