#CurrencyPairPrediction Concrete Example Suppose you build a deep LSTM network for predicting EUR/USD short-term returns. • You train it on 5 years of tick data. • It learns: • Price reversals. • Mean reversion signals. • High-impact news reaction patterns. Now: You want to predict GBP/USD or AUD/USD. Rather than training a new LSTM from scratch: • Freeze early LSTM layers (because basic price pattern learning is general). • Fine-tune output layers on GBP/USD recent data. Result: • Much faster convergence. • Better performance (because the model already “understands” forex structure).