# Enhancing Social Media Platforms with Machine Learning Algorithms and Neural Networks
## 2 Theoretical Background  
### Machine Learning and Neural Networks  
#### 2.1.1 Overview of Machine Learning  
AI is a subfield of computer science that incorporates a wide range of computational operations, from algorithmic production to machine learning and deep learning methods [29]. Traditional AI problem-solving methods are based on if-then rules, whereas machine learning and deep learning seek to iteratively evolve comprehension of a data set without explicitly coding any rules [30; 31]. This permits the computing system on which they are implemented to automatically acquire knowledge and develop predictions beginning with a set of input data, adjusting the parameters by optimizing the performance standard defined on the data and decreasing the error rate at every phase of the learning process [32].  
In other words, the objective of machine learning is to create software that adapts and learns on its own, i.e., without a pre-programmed system dictating its behavior. Due to the fact that training data are used as examples, algorithms can learn from their errors.  
Consequently, the amount a model learns is contingent on the quality and quantity of the example information to which it has been exposed [33].  
Machine learning provides a variety of mathematical tools for solving a broad range of problems. Artificial neural networks, which are trained to solve a specific task, are currently the most ubiquitous and essential tool. Neurons are organized into layers and precisely connected to form a network. As previously stated, a neural network is considered \"deep\" when the number of layers is considerable. The approach of deep learning attempts to mathematically model how the human brain processes data from vision and hearing: the stimuli of the ears and eyes, passing through the human brain, are broken down into simple concepts and gradually reconstructed into increasingly complex and abstract visualizations [32; 34].  
Similarly, in a deep neural network, a visage is represented as an array of pixel values. The initial layer can readily identify edges with varying orientations. Subsequent layers combine these elements to create rounded corners and extended contours. By locating specific groups of contours and corners, subsequent layers can detect entire portions of specific objects. These are then combined with additional processing layers to enable us to represent the faces we wish to learn [35; 36; 32].  
Machine learning is an approach to developing data-driven AI. It is a subset of AI with many assets, and employs predictive statistical techniques [37]. It was created in the 1940s, but it has only recently been possible to incorporate it into daily routines. In general, machine learning algorithms employ both unsupervised and supervised learning techniques. Unsupervised learning does not require human feedback, whereas supervised learning does [38]. The most significant strengths of machine learning are its methodology and reinforcement learning. The machine learning methodology entails training an algorithm to recognize patterns in new data sets [39]. Reinforcement learning is a subfield of machine learning in which an intelligent system acquires knowledge through trial and error by receiving incentives or penalties for its actions.