Handbook on Fundamentals and Methods of Machine and Deep Learning (Volume - 1)

Articles

• Meta-Learning: Learning to Learn in Machine Learning

  T.V.S. Padmaja

  1-5

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• AutoML: Automating the Machine Learning Pipeline

  Ms. Aayushi Jain

  6-11

  • PDF

• Quantum Machine Learning: A review of Emerging Concepts

  Nandkishor Dault Aher, Rani Nandkishor Aher

  12-17

  • PDF

• Adversarial Attacks and Defenses in Deep Learning Systems

  Endluri Venkata Naga Jyothi

  18-23

  • PDF

• Deep Learning for Image Classification: A Review

  EMN. Sharmila

  24-29

  • PDF

• Transforming Education: Optimizing Learning Environments through the Fusion of Signal Processing and IoT

  Harsha Grandhi

  30-36

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• Overview on Adversarial Attacks and Defenses in Deep Learning Systems

  Nilima D.Bobade, Swati S. Sherekar

  37-41

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• Reinforcement Learning: From Theory to Applications

  Kumari Priyanka Sinha

  42-47

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• AutoML: Streamlining and Automating the Machine Learning Pipeline

  Muthukumar Subramanian

  48-52

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• Transfer Learning: Bridging Domains in Machine Learning

  Gayathri Devi

  53-58

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• Meta-Learning: Teaching Machines How to Learn More Efficiently

  S. Radhimeenakshi

  59-63

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• Natural Language Processing with Deep Learning: Recent Advances

  Levina Tukaram

  64-69

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• Evolution of Machine Learning Algorithms A Comprehensive Review

  D. Venkata Lakshmi

  70-80

  • PDF