Machine Learning and Deep Learning: Algorithms and Applications

Abstract

Machine learning is a subfield of artificial intelligence (AI) that involves the development of algorithms that can learn patterns from data and make predictions or decisions without being explicitly programmed. Deep learning is a subset of machine learning that uses deep neural networks with multiple layers to learn and extract features from complex data. The history of machine learning can be traced back to the 1950s, with the development of perceptrons, a type of artificial neuron. However, progress was slow until the 1990s, when the availability of large datasets and more powerful computing resources enabled the development of more sophisticated algorithms. In recent years, deep learning has achieved remarkable success in a wide range of applications, such as image recognition, speech recognition, and natural language processing. Machine learning and deep learning are important because they enable computers to perform tasks that were previously thought to be the exclusive domain of humans. They have the potential to revolutionize many industries, such as healthcare, finance, and transportation, by automating tasks and improving decision-making. However, they also raise ethical and societal concerns, such as bias and job displacement, that need to be addressed.

Cite this paper

@incollection{rekha2023machine,
  title = {{Machine Learning and Deep Learning: Algorithms and Applications}},
  author = {Rekha, V. and S., Uma Shankari},
  year = {2023},
  doi = {10.59646/csebookc1/004}
}

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