Machine learning paradigm and application area of deep learning and types of neural network

Abstract

Machine learning and deep learning are rapidly evolving paradigms that are being used to solve a wide range of problems in various application areas. This literature review summary highlights the different types of neural networks and tools used in machine learning, as well as the use cases for deep learning, such as image identification, natural language processing, audio recognition, anomaly detection, and recommender systems. The existing system of machine learning is constantly expanding, with new techniques and architectures being developed to address real world problems. A proposed system for machine learning involves identifying the problem, collecting and preparing data, selecting and training appropriate models, evaluating performance, deploying and monitoring the model, and continuously updating it to improve its accuracy and effectiveness.