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LightAutoML: AutoML Solution for a Large Financial Services Ecosystem

Unite.AI

Second, the White-Box Preset implements simple interpretable algorithms such as Logistic Regression instead of WoE or Weight of Evidence encoding and discretized features to solve binary classification tasks on tabular data. In the situation where there is a single task with a small dataset, the user can manually specify each feature type.

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Natural Language Processing Examples: 5 Ways We Interact Daily

Defined.ai blog

That’s the power of Natural Language Processing (NLP) at work. In this exploration, we’ll journey deep into some Natural Language Processing examples , as well as uncover the mechanics of how machines interpret and generate human language. What is Natural Language Processing?

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Top TensorFlow Courses

Marktechpost

Learning TensorFlow enables you to create sophisticated neural networks for tasks like image recognition, natural language processing, and predictive analytics. It covers various aspects, from using larger datasets to preventing overfitting and moving beyond binary classification.

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An Overview of the Top Text Annotation Tools For Natural Language Processing

John Snow Labs

In this article, we will discuss the top Text Annotation tools for Natural Language Processing along with their characteristic features. Overview of Text Annotation Human language is highly diverse and is sometimes hard to decode for machines. Below are some features of Prodigy: – It is suitable for novice users.

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This AI Paper Unveils X-Raydar: A Groundbreaking Open-Source Deep Neural Networks for Chest X-Ray Abnormality Detection

Marktechpost

A custom-trained natural language processing (NLP) algorithm, X-Raydar-NLP, labeled the chest X-rays using a taxonomy of 37 findings extracted from the reports. The X-Raydar achieved a mean AUC of 0.919 on the auto-labeled set, 0.864 on the consensus set, and 0.842 on the MIMIC-CXR test.

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Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

AWS Machine Learning Blog

Customers can create the custom metadata using Amazon Comprehend , a natural-language processing (NLP) service managed by AWS to extract insights about the content of documents, and ingest it into Amazon Kendra along with their data into the index. Custom classification is a two-step process.

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Deep Learning in Healthcare: Challenges, Applications, and Future Directions

Marktechpost

CNNs excel in tasks like object classification, detection, and segmentation, achieving human-level accuracy in diagnosing conditions from radiographs, dermatology images, retinal scans, and more. Deep Learning in Medical Imaging: Deep learning, particularly through CNNs, has significantly advanced computer vision in medical imaging.