Gradient perturbation enhances data privacy in AI by injecting noise into gradient updates, ensuring compliance with regulations like GDPR and the Australian Privacy Act. Widely used in healthcare, education, and energy sectors, it balances privacy and utility for secure machine learning.
Adam, a powerful optimisation algorithm introduced in 2014, is widely used in deep learning for its adaptive learning rates and momentum integration. Its applications range from healthcare analytics to public transport planning, making it a cornerstone of modern AI.
AdaBoost, introduced in 1995 by Freund and Schapire, transforms weak learners into powerful ensemble models, delivering high predictive accuracy. Widely used in healthcare, traffic optimisation, and education, it remains a cornerstone in machine learning applications.
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