An Analysis Of The Distinction Between Deep Learning And Machine Learning

Abstract

Machine learning and deep learning, two cutting-edge areas of computer science, are making huge waves in the corporate sector. Machine learning refers to the process of instructing computers and other devices to draw conclusions from past data or actions by analyzing examples stored in their memory. To train and learn from unstructured data, deep learning applies methods and techniques from artificial neural networks, which is a subfield of machine learning. This makes it possible to learn from disorganized data. Data utilization and management strategies that are highly automated and technologically sophisticated are urgently needed so that the researchers can make sense of the ever-increasing mountain of data. Machine learning (ML) and deep learning (DL) software is investigated extensively in this study. This research provides a foundational overview of ML and DL. Following this, researchers go into the most popular methods and approaches in domains where technical progress has opened new possibilities. To wrap things off, The researchers provide a commercial perspective on the two most popular ML and DL applications.