What is the difference between CNN, RNN, and Transformer models?

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CNN, RNN, and Transformer are three major types of neural network architectures, each designed for different data patterns.

• Convolutional Neural Networks (CNNs):

  • Best for spatial data like images.

  • Use convolution layers to capture local features (edges, textures) and pooling layers for dimensionality reduction.

  • Strength: Great at recognizing patterns regardless of position (translation invariance).

  • Limitation: Not ideal for sequential or long-range dependencies.

• Recurrent Neural Networks (RNNs):

  • Designed for sequential data like text, speech, or time series.

  • Process input step by step, maintaining a hidden state to “remember” past information.

  • Variants like LSTM and GRU solve short-term memory and vanishing gradient issues.

  • Limitation: Struggle with very long sequences due to sequential processing.

• Transformers:

  • Introduced in 2017 (“Attention is All You Need”).

  • Use self-attention mechanisms to capture relationships between all tokens at once, regardless of distance.

  • Enable parallel processing, making them faster and more scalable than RNNs.

  • Power modern NLP (BERT, GPT) and even vision models (ViT).

  • Strength: Handle long-range dependencies very effectively.

👉 Summary:

• CNN → Best for images (spatial patterns).

• RNN → Best for sequences (step-by-step context).

• Transformer → Best for long-range, parallel processing (state-of-the-art in NLP & beyond).

Would you like me to also make a comparison table (CNN vs RNN vs Transformer) for quick revision?

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