The Evolution of Machine Learning: From Traditional Algorithms to Deep Learning Paradigms
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
CRC Press
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
There has been a noticeable development in the area of machine learning (ML) over the last few decades, transitioning from conventional algorithm-based systems to neural networks. The goal of this chapter is to portray the evolution of machine learning, highlighting important steps, primary algorithms, and the development of mono-approach neural network modeling. We examine the various methodologies developed in the field, including supervised, unsupervised, and reinforcement learning, and explain how deep learning architectures have transformed image recognition and natural language processing, and autonomous systems. The chapter concludes by addressing existing issues in machine learning, most notably interpretability, bias and the computational complexity, while suggesting directions for future research in this active field. © 2025 Laith Abualigah.
Açıklama
Anahtar Kelimeler
Deep Learning, Deep Reinforcement Learning, Image Processing, Learning Algorithms, Learning Systems, Natural Language Processing Systems, Neural Networks, Reinforcement Learning, Conventional Algorithms, Interpretability, Language Processing, Learning Architectures, Learning Paradigms, Machine-Learning, Natural Languages, Neural Network Model, Neural-Networks, Reinforcement Learnings, Image Recognition
Kaynak
A to Z of Deep Learning and AI
WoS Q Değeri
Scopus Q Değeri
N/A
