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Next comes the training phase. We feed the historical data into the CNN, and the network adjusts its internal parameters to minimize the difference between its predictions and the actual stock prices. This is an iterative process, where the CNN learns from its mistakes and gradually improves its forecasting accuracy. It's like showing the CNN a series of historical **TGT stock charts** and telling it, "This is what happened next." Over time, the CNN learns to recognize the patterns that lead to specific price movements. The more data we feed the CNN, the better it becomes at predicting **TGT's stock price**.
