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DecFrom healthcare and education to retail and manufacturing, accurate and efficient data annotation is the cornerstone of building intelligent systems. These advances would play a crucial role in driving progress and improving lives. Amidst all this, inaccurate or poorly labeled data can lead to biased models, flawed decision-making, and, ultimately, negative consequences. This is where data annotation […]
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NovNatural Language Generation aka NLG models are designed to generate human-like text and are trained on vast datasets. They have become integral to various applications, from chatbots and virtual assistants to content generation and data summarization. Data annotation in the context of NLG involves labeling or marking data to provide context, structure, and meaning to the training[…]
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NovData annotation is a critical step for artificial intelligence’s development as it involves labeling and tagging data to train algorithms. Among its types, crowdsourcing data annotation is an essential variety, which involves outsourcing the labeling task to a large group of contributors instead of hiring fixed annotators. Crowdsourcing data annotation has gained popularity as it is cost-effective[…]
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NovTransfer learning is a machine learning data annotation technique where an AI model trained on one task is adapted for a somewhat related task. In simple words, instead of starting from scratch, using a pre-trained model as a starting point and fine-tuning it as per the specific task is transfer learning. This concept has been utilized by[…]
