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@article{165992,
        author = {Prakruti D. Dave and Ankit J. Solanki and Hiren S. Lekhadiya},
        title = {A Comprehensive Assessment of Optimisation Methods for Machine Learning Instruction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {1},
        pages = {2320-2325},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165992},
        abstract = {An extensive review of optimisation methods for training machine learning (ML) models a crucial branch of artificial intelligence is provided in this article. ML uses statistical techniques to allow systems to learn from experience and become more intelligent without the need for explicit programming. The study emphasises the value of optimisation in machine learning, emphasising how it can be used to improve training efficiency and generalisation by modifying model parameters to minimise loss functions. Numerous optimisation techniques are examined, such as Constraint-based techniques, Gradient Descent Variants, Adaptive Learning Rate Techniques, Second-Order Optimisation Techniques, and Bayesian Optimisation. Every segment delves into the fundamentals, uses, and advantages of these methods, highlighting their significance in addressing issues like overfitting, scalability, and computational effectiveness. The purpose of this page is to help practitioners, academics, and enthusiasts navigate the wide range of optimisation approaches designed for various machine learning algorithms and applications.},
        keywords = {Optimization, Adaptive, Second-Order Optimization, Constraint-based Methods, Overfitting, Scalability, Artificial Intelligence.},
        month = {June},
        }