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For all the excitement about machinelearning (ML), there are serious impediments to its widespread adoption. Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML.
In this article, we’ll discuss the challenge organizations face around fraud detection, how machinelearning can be used to identify and spot anomalies that the human eye might not catch. Now that we are satisfied with how the model performs, we can persist it and use it from other notebooks / scoring scripts.
word2vec is an unsupervised learning technique—that is, it is applied to a corpus of natural language without making use of any labels that may or may not happen to exist for the corpus. With the SG architecture, context words are predicted given the target word. Journal of MachineLearning Research, 9, 2579–605.].
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