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

Marktechpost

Recent advancements in deep learning offer a transformative approach by enabling end-to-end learning models that can directly process raw biomedical data. Despite the promise of deep learning in healthcare, its adoption has been limited due to several challenges.

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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

TensorFlow is a powerful open-source framework for building and deploying machine learning models. Learning TensorFlow enables you to create sophisticated neural networks for tasks like image recognition, natural language processing, and predictive analytics.

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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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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. Finally, the CV Preset works with image data with the help of some basic tools.

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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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Understanding Graph Neural Network with hands-on example| Part-1

Becoming Human

Photo by NASA on Unsplash Hello and welcome to this post, in which I will study a relatively new field in deep learning involving graphs — a very important and widely used data structure. This post includes the fundamentals of graphs, combining graphs and deep learning, and an overview of Graph Neural Networks and their applications.