Fine-Tuning

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Fine-Tuning

Fine-Tuning refers to the technique of initializing a network with parameters from another task (such as an unsupervised training task), and then updating these parameters based on the task at hand. For example, NLP architecture often use pre-trained word embeddings like word2vec, and these word embeddings are then updated during training based for a specific task like Sentiment Analysis.