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Deep Neural Networks is a type of Artificial Neural Network(ANN) that consists of multiple layers of interconnected nodes, each node building upon the previous layer to refine and optimize the prediction or categorization. A deep neural network implies flexible handling capacity for highly complex data with sophisticated mathematical modeling of deep learning models. A deep neural network transforms data into a highly creative and abstract component. Deep Neural Networks(DNN) have three layers include input layer, an output layer, and multiple hidden layers. A fully connected deep neural network contains an input layer or visible layer where the information is known, the number of hidden layers where each node is a hidden node and applies weight to input, output layer which is directly linked to the target value that the model attempts to predict. Data provides each node with information in the form of inputs, and the node multiplies the inputs with random weights, calculates them, and adds a bias. Deep Neural Networks can deal with linear or non-linear problems by computing the probability of each output layer by layer through appropriate activation functions, and these activation functions determine the neuron is to activate or not. The deep neural network techniques enable well-organized processing to improve energy efficiency without losing accuracy with high hardware costs. Recurrent Neural Networks, Convolutional Neural Network, Deep Neural Network, Deep Belief Network, Generative Adversarial Network, Radial Basis Function Networks, Restricted Boltzmann Machine, and Autoencoder are the categories of Deep neural networks. The most popular applications of deep neural networks are speech recognition, image recognition, healthcare, self-driving cars, Natural Language Processing and Currently, deep neural networks are also used in many artificial intelligence applications such as computer vision and robotics. Recent research on deep neural networks are convolutional neural networks with transforms, contrastive learning with aligning vector representation in neural networks, self-supervised learning, video to video synthesis, Fine-tuning the universal language model for text classification, and Modeling the structure of space of visual tasks