{"id":13311,"date":"2024-01-02T13:41:52","date_gmt":"2024-01-02T13:41:52","guid":{"rendered":"https:\/\/www.temok.com\/blog\/?p=13311"},"modified":"2024-01-02T13:50:52","modified_gmt":"2024-01-02T13:50:52","slug":"semi-supervised-learning","status":"publish","type":"post","link":"https:\/\/www.temok.com\/blog\/semi-supervised-learning\/","title":{"rendered":"Semi Supervised Learning: Optimizing Models With Limited Labels"},"content":{"rendered":"<\/span> 7<\/span> min read<\/span><\/span>

As machine learning professionals, we can quickly become drowning in what we can’t utilize and yearning for what doesn’t exist. On the other hand, supervised learning is the backbone of machine learning (ML) approaches, yet it relies on labeled data, which is time-consuming and costly to annotate. On the other hand, unsupervised learning uses unlabeled data, which is typically abundant in the absence of human-made annotations except for semi supervised learning.<\/p>\n

One of these methodologies is frequently insufficient to educate a model to deploy-ready standards when employed alone. Labeling a large dataset takes time and money, and unlabeled data might not deliver the needed accuracy.<\/p>\n

What if we had access to both sorts of information? What if we need to label a portion of our dataset? How can we increase model performance by combining our labeled and unlabeled datasets?<\/p>\n

We may employ semi-supervised classification\u00a0intuition to address these issues, which uses unlabeled and labeled information to improve model performance.<\/p>\n

So, continue reading and exploring to learn more about semisupervised learning to optimize different models with limited labels.<\/p>\n

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