The Core Problem
Feedforward networks process each input independently. They have no memory of previous inputs, making them unsuitable for sequential data where context and order matter.
Why This Matters:
• Understanding "bank" requires knowing if we're talking about money or rivers
• Predicting stock prices needs historical context
• Language translation depends on sentence structure
• Each input provides valuable context for the next
🔍 Real Example:
"The bank can guarantee deposits will eventually yield a profit" - Which bank? Financial or river bank? Context from earlier sentences is crucial!
Key Limitations
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🧠
No Memory
Cannot remember previous inputs or learn from sequence history
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📏
Fixed Input Size
Requires same-length inputs, can't handle variable sequences
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🔄
Order Doesn't Matter
Treats [A,B,C] same as [C,A,B] - loses sequential information
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🎯
No Context Sharing
Each prediction is independent, no information flow between steps
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⚡
Poor Scalability
Network size grows exponentially with sequence length