# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 3 From Machine Learning to Deep Learning  
Machine learning consists of computer programs that learn from data instead of pre-programmed instructions. Machine learning tasks are commonly categorized into three different types: supervised learning, unsupervised learning, and reinforcement learning. More recently, there has been an increasing interest in combining supervised and unsupervised approaches to form semi-supervised learning. In supervised learning, the task is to learn the mapping from input \(\mathbf{x}\) to output \(y\), given a dataset of \((\mathbf{x},y)\) pairs. A typical problem in supervised learning is classification. In classification, every sample \(\mathbf{x}\) in the dataset belongs to one of \(M\) classes. The class of each sample is given by a label that has a discrete value \(\{0,\ldots,M-1\}\). When the labels in the above example are real values (continuous), this task is known as regression problem. In unsupervised learning, the dataset only consists of samples and does not have labels. Typically, the aim of unsupervised learning is to find patterns that have some form of regularity. Usually, some model is fit to the data with the goal of modeling the input distribution. This is known as density estimation in statistics. Unsupervised learning can also be used for extracting features for supervised learning. In semi-supervised learning, the number of labeled samples is too small, while there is usually a large number of unlabeled samples. This is also a typical case in real-world situations. The goal of semi-supervised learning is to find a mapping from \(\mathbf{x}\) to \(y\) but also to somehow make the unlabeled information useful for the mapping task. At least, the performance of the semi-supervised model needs to improve compared to the supervised model.  
Recently, the field of deep learning or deep neural networks has received a lot of attention from the machine learning community due to its success and performance in many machine learning tasks. Deep learning has surpassed the traditional machine learning techniques because of its greater computation power, automatic feature extraction, and the development of GPUs. Deep learning addresses a famous problem in machine learning called the problem of representation.The motivation behind deep learning is to model how the human brain works. As an example, let us consider the problem of recognizing a face using data in the form of images. We know that some of the important features in a human face are the nose, eyes, and mouth. However, looking at just one single pixel or even a set of pixels of the image tells very little about the image. However, on the other hand, if we transform the data many times, we might be able to tell what the image is, because of the high number of representations we get in the form of important features. Deep learning has been able to achieve major breakthroughs in speech recognition, object recognition, image segmentation, and machine translation [12]. The scientific contributions and developments in the deep learning method and the structure of the models have provided the ground work for the advances in such a large variety of applications [12].Therefore, it may be possible for cellular networks to obtain the benefits of deep learning research, especially in the era of 5G [13,14].