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Simplify data prep for generative AI with Amazon SageMaker Data Wrangler

AWS Machine Learning Blog

While this data holds valuable insights, its unstructured nature makes it difficult for AI algorithms to interpret and learn from it. According to a 2019 survey by Deloitte , only 18% of businesses reported being able to take advantage of unstructured data. Clean data is important for good model performance.

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Text to Exam Generator (NLP) Using Machine Learning

Mlearning.ai

I came up with an idea of a Natural Language Processing (NLP) AI program that can generate exam questions and choices about Named Entity Recognition (who, what, where, when, why). I let only the word with the pos of NOUN, VERB, ADJ, and ADV to pass through the filter and continue to the next process.

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Introduction to Autoencoders

Flipboard

During training, the input data is intentionally corrupted by adding noise, while the target remains the original, uncorrupted data. The autoencoder learns to reconstruct the clean data from the noisy input, making it useful for image denoising and data preprocessing tasks.

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Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

Advances in neural information processing systems 32 (2019). Visualizing data using t-SNE.” He is broadly interested in Deep Learning and Natural Language Processing. He started at the NFL in February 2020 as a Data Scientist and was promoted to his current role in December 2021. He obtained his Ph.D.

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