# A Review of Deep Learning Applications for the Next Generation of Cognitive Networks
## 1 Introduction
However, it is essential to point out that, in essence, the ML domain is a combination of probability, statistics, and computer sciences that allows the development of stochastic algorithms designed so that they can learn and improve over time when exposed to new data. For this reason, ML methods are defined as applying statistical methods to identify patterns in data using computers [6] and as methods that can learn from data and detect hidden patterns in databases to use generated knowledge to predict new outputs of the system [7]. The fundamentals of ML methods are varied, but our focus in this survey is on DL methods and a particular type of ML methods. DL models are different from most ML methods since their functioning is inspired by the functioning of the human brain. The power of the human brain resides in the fact that it is composed of around \(10^{11}\) neurons. Neurons work in parallel with the memory processing the information captured by the synapses to be distributed over the network [8]. For this reason, an artificial neural network is described as a collection of simple pieces (usually adaptable) that are massively interconnected in parallel and organized hierarchically to interact with real-world objects in the same way that the human nervous system does [9]. DL is defined as a generalization of artificial neural networks (ANN) where more than one hidden layer is used, implying that more neurons are used to implement the model. The adjective \"deep\" applies not to the acquired knowledge itself but to how the knowledge is acquired [10] since it stands for the idea of successive layers of representations. The \"deep\" of the model refers to the number of layers that contribute to a model. This means that DL is a type of ML technique that utilizes a stack of multiple processing layers. Each successive layer uses the output from the previous layer as input for learning representations of data with various levels of abstraction. DL is a type of universal learning that may be used to solve supervised, semi-supervised, and unsupervised issues. Training data containing pairs of objects are required for the supervised framework (typically vectors). The input data are one component (predictors). The other is the desired outcome (response variable = output); ML supervised approaches learn a function that translates an input to an output based on input-output pairings. The function's output can be a continuous value (as in regression issues), a class label (as in binary and multinomial regression), or a count value (as in Poisson regression). This means that ML methods allow the creation of machines for predicting many types of univariate and multivariate outcomes. Unsupervised DL algorithms, on the other hand, only have input (predictors) data (X) and no labeled outputs or response variables (y). Therefore, their goal is to extract the underlying structure or distribution in the data to understand more about it. However, we do not know if our work is accurate since we do not know if the correct answer was complete without supervision. Unlike supervised and unsupervised methods, the semi-supervised methods have few observations with their respective inputs (X) and their respective output label (Y). Still, most of the statements do not have output labels. In this way, these methods try to work with fewer data for training and, therefore, less processing time, seeking to solve two of the main problems of supervised methods and, on the other hand, to increase the low efficiency of non-supervised methods. Although three approaches have been used in many domains, we will focus primarily on applications of supervised methods mainly used for prediction purposes. The value of DL as a tool for designing AI systems, goods, gadgets, and apps is well-documented. Since technical applications are used in agriculture, banking, medicine, computer vision, and natural language processing, these items can be found everywhere, from social sciences to natural sciences.