How do DNNs make predictions?

Deep Neural Networks (DNNs) make predictions by leveraging their architecture and training process. DNNs consist of layers of interconnected nodes, each node performing computations on the input it receives. During training, these nodes learn to recognize patterns in the data by adjusting their internal parameters. When making predictions, DNNs forward propagate the input data through the network, allowing the nodes to process and transform the information. As the input traverses through layers, it undergoes multiple non-linear transformations, enabling the extraction of higher-level features from the raw data. The final layer of the DNN produces the prediction based on the learned representations. The strength and accuracy of these predictions rely on the inherent ability of DNNs to learn complex patterns and relationships within the data during the training process.

This mind map was published on 17 October 2023 and has been viewed 98 times.