Dear Doctor
"DDNs consist of input, hidden and output layers. Input nodes act as a layer to place input data. The number of output layers and nodes required change per output. For example, yes or no outputs only need two nodes, while outputs with more data require more nodes."
Your work is really very interesting and useful .
my sincere congratulations!!
Describing the learning process of a deep network in a nutshell is complicated. In essence, a deep network is a classical dense network, but one that incorporates three or more hidden layers. Deep neural networks work by structuring layers of interconnected nodes, or neurons, within an architecture that facilitates learning and prediction from input data. Each layer processes information and transmits it to the next layer via weighted connections. While the initial layers capture fundamental features, deeper layers extract more intricate and abstract representations. The training process consists of adjusting the weights based on the network output compared to the desired output, optimizing the model for accurate predictions.
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