Deep Neural Networks (DNNs) are neural networks that have more than one hidden layer. By increasing the number of layers in the network, DNNs are able to learn increasingly complex representations of the data and can achieve higher levels of accuracy on many tasks.
The key feature of DNNs is their ability to automatically learn feature hierarchies from the data. Each layer in the network learns features that are increasingly complex and abstract, building on the features learned in the previous layer. This allows the network to represent complex relationships in the data and make accurate predictions.
DNNs can be used for a wide range of tasks, including image and speech recognition, natural language processing, and even game playing. They are often trained using large datasets and require significant computational resources, but can achieve state-of-the-art results in many applications.
One of the main challenges with DNNs is the risk of overfitting, where the network learns to memorize the training data instead of generalizing to new data. To address this, techniques such as regularization, dropout, and early stopping can be used during training to prevent overfitting and improve generalization.
Overall, DNNs are a powerful tool for solving complex problems and achieving high levels of accuracy on a variety of tasks. They have been used successfully in many applications, from computer vision to natural language processing, and continue to be an active area of research in machine learning.