Machine Learning and Deep Learning for Big Data Analysis

Abstract

As data plays a role in machine learning and provides insights across various sectors, organizations are placing more emphasis on collecting, organizing, and managing information. However, traditional methods of analysing data struggle to keep up with the increasing complexity and volume of big data. To extract insights from datasets, advanced techniques like machine learning and deep learning have emerged. In the field of self-driving cars, analysing sensor data relies on methodologies developed from data analytics. These trends extend beyond cases; big data and deep learning are driving forces supported by enhanced processing capabilities and the expansion of networks. Managing the complexities involved in processing amounts of data requires scalable architectures that leverage distributed systems, parallel processing techniques and technologies such as GPUs. This development is particularly relevant for industries like banking, healthcare, and public safety, which have pressing demands, for transparency and interpretability in models.