How does a DNN work?

A Deep Neural Network (DNN), also known as a deep learning model, is a type of artificial neural network that emulates the workings of the human brain to perform complex tasks. It consists of multiple interconnected layers of neurons, each layer extracting different levels of abstraction from the input data. The process starts with the input layer where the raw data is fed, such as images or text. The data then traverses through multiple hidden layers, with each layer applying nonlinear transformations to the input and learning feature representations. These hidden layers help the network recognize patterns, features, and relationships within the data. Finally, the output layer produces the desired output based on the learned features. The learning process occurs through a technique called backpropagation, where the network adjusts its internal parameters iteratively to minimize the difference between predicted and actual outputs. DNNs have proven highly successful in various domains, including computer vision, speech recognition, and natural language processing.

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