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AI & Engineering Academics›🏕️ AI Canopy›Lessons›Deep Neural Networks - CNNs, RNNs, and Transformers Explained
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AI Canopy • Advanced⏱️ 20 min read

Deep Neural Networks - CNNs, RNNs, and Transformers Explained

Public lesson preview

Understand the three major deep learning architectures and why transformers came to dominate modern AI.

Programme
AI Canopy
Level
Advanced
Reading time
20 min read

Learning objectives

By the end of this lesson, you will understand:

  • ✓Convolutional Neural Networks (CNNs)
  • ✓Recurrent Neural Networks (RNNs) and LSTMs
  • ✓Transformers - The Architecture Behind Everything
  • ✓Why Transformers Won
  • ✓Modern Developments

Lesson outline

  1. Convolutional Neural Networks (CNNs)
  2. Recurrent Neural Networks (RNNs) and LSTMs
  3. Transformers - The Architecture Behind Everything
  4. Why Transformers Won
  5. Modern Developments

Introductory preview

Three architectures have shaped modern deep learning: CNNs for spatial data, RNNs for sequences, and Transformers for nearly everything. Understanding their strengths and trade-offs is essential for any advanced AI practitioner.

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