Managing Deep Learning Model

Model storage and retrieval is key in neural networks for storing and reusing trained models. After training, a model has learned weights and biases. We store these in files to avoid retraining each time. This makes it easy to share and use the model in different applications. We can load the model from these files for prediction or further training, saving time and computational resources. Libraries like TensorFlow and PyTorch provide easy functions for these tasks, helping developers manage their neural networks effectively.

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