whether forward and predict in deep learning are the same
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'forward' is used to compute deep learning network output for TRAINING whereas 'predict' is used to compute deep learning network output for INFERENCE.
In some cases like yours, both may give similar results, however some deep learning layers behave differently during training and inference (prediction). For example, during training, dropout layers randomly set input elements to zero to help prevent overfitting, but during inference, dropout layers do not change the input.
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I am assuming with 'forward', you mean 'forward propagation'. Deep learning networks usually have a lot of layers. Each layer accepts input data, processes it as per the activation function and passes it to the next layer. This is called 'forward propagation'.
In an abstract way, you can say 'prediction' is something that happens with the last layer where we get the final results from our deep learning network.
So 'prediction' and 'forward propagation' have a minor difference between them.