Authors: Vikash Sharma, Dr. Ramesh Patil
Abstract: The advancement of artificial intelligence (AI) has given rise to two major approaches: traditional machine learning (ML) and deep learning (DL). While traditional ML relies on feature engineering and structured learning approaches, deep learning automates feature extraction through artificial neural networks. This paper explores the differences between these methods, compares their performance across domains such as image recognition, natural language processing, and financial forecasting, and evaluates their advantages and limitations. Experimental results and literature reviews indicate that deep learning excels in handling large datasets and complex patterns, whereas traditional ML is more suitable for smaller datasets with structured features.
DOI: http://doi.org/
