DNN (Deep Neural Network)

Information

• DNN (Deep Neural Network) is a type of artificial neural network with multiple layers between the input and output layers.
• DNNs are used for various machine learning tasks, such as image and speech recognition, natural language processing, and autonomous systems.
• DNNs use backpropagation, a supervised learning algorithm, to adjust the weights of each layer to minimize the difference between the predicted output and the actual output.
• DNNs can have hundreds or thousands of hidden layers, making them capable of learning complex relationships in the data and extracting high-level features automatically.
• DNNs can use various activation functions, such as ReLU, sigmoid, and tanh, to introduce non-linearity and enable the network to learn non-linear relationships between the input and output.
• DNNs can use various regularization techniques, such as dropout, weight decay, and early stopping, to prevent overfitting and improve generalization.
• DNNs require large amounts of data and computation power to train, making them suitable for big data applications.
• DNNs can be trained on GPUs and TPUs to speed up the training process.
• DNNs can be fine-tuned using transfer learning, where pre-trained models are used to extract features from new data and train a new output layer.
• DNNs have achieved state-of-the-art performance on many machine learning benchmarks and have been widely used in various industries, such as healthcare, finance, and e-commerce.