3 Review(s)

Recent innovations in machine learning (ML) focus on improving accuracy, efficiency, and adaptability across diverse domains. Techniques such as deep learning, reinforcement learning, and transfer learning have transformed automation and intelligent decision-making. Neural architecture search and federated learning enhance model optimization and data privacy. Additionally, explainable AI (XAI) promotes transparency in complex models, while edge computing enables real-time analytics closer to data sources. Generative AI and self-supervised learning are pushing boundaries in creativity and autonomy. Together, these advancements make ML systems more powerful, scalable, and trustworthy for applications in healthcare, finance, robotics, and beyond.