TensorFlow Similarity Boost Machine Learning Model’s Accuracy Using Self-Supervised Learning

The practice of identifying raw data (such as pictures, text files, videos, etc.) and adding relevant and informative labels providing context to the given data is known as data labeling. It is employed to train the machine learning model in many use cases. For example, labels can be used in computer vision to identify whether a photograph has a bird or an automobile, in speech recognition to determine which words were spoken in an audio recording,

Overall, labeled datasets help train machine learning models to recognize and understand recurrent patterns in the input data. After being trained on labeled data, the ML models are able to recognize the same patterns in new unstructured data and produce reliable results. Continue Reading

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