Building More Efficient AI Models through Unsupervised Representation Learning
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I3P112Keywords:
AI models, unsupervised learning, representation learning, clustering, anomaly detection, feature extraction, machine learning, deep learning, data mining, self-supervised learning, neural networks, dimensionality reduction, pattern recognition, data segmentation, unsupervised feature learning, generative models, unsupervised pretraining, data clustering algorithms, latent variable models, knowledge discovery, predictive modeling, data analysis, AI-driven decision making, automated feature selection, reinforcement learning, model optimization, unsupervised neural networksAbstract
Artificial Intelligence (AI) has advanced in a rapid and exponential manner and AI is now revolutionizing the medical field, the development of self-driving cars, and the management of financial operations. A major reason for such achievement is the efficiency and performance of AI models, which can be drastically boosted through new learning methods. One of the most promising avenues is the unsupervised representation learning method that also offers the choice of leaping over the traditional supervised learning. Instead of supervised learning, where labeled data is the base upon which training of models takes place, unsupervised learning allows AI systems to get new insights out of raw, unlabeled data without any human intervention. This method teaches the AI system how to represent the data in an organized manner, thus enabling it to uncover hidden features and relations without anyone helping it. In effect, AI models, powered by unsupervised representation learning, can be quite successful in areas like clustering, anomaly detection, and feature extraction, frequently beating traditional methods in terms of their speed and accuracy. .The skill of finding deep structures in a dataset can unexpectedly influence many areas of the sciences, e.g., it can help us create a better diagnostic system in medicine or use it for decision-making when investing in the stock market
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