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Crayon
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Özgür SahinBuild literate, language understanding apps.Train custom machine learning models for iOS development.Develop intelligent apps that read text and answer questions
LEER
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Irascible
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https://doi.org/10.1007/978-1-4842-6421-8Text Classification; Natural Language Processing; NLP; Swift; iOS; iPhone; iPad; Sentiment analysis; CreateM
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集聚成团
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Introduction to Apple ML Tools,ls, you may waste a lot of time. This chapter will introduce the tools Apple provides to build ML applications easily for iOS developers. The frameworks and tools introduced in this chapter are Vision, VisionKit, Natural Language, Speech, Core ML, Create ML, and Turi Create. We will learn what capab
triptans
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Text Classification,at has a variety of use cases. One of the famous uses is classifying text into emotional categories (positive, negative, etc.) which is called sentiment analysis. This method can be used on any text data that has been categorized. Text classification allows us to find the author of a piece of writin
babble
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Text Generation,nificantly improved. These models often benefited from recurrent neural networks or transformers. In this chapter, we will learn how to use one of the best text generation models (GPT-2) and build an iOS application using this model. Our application will use built-in OCR capabilities to capture text