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Digging Into Various Deep Learning Models

Pickl AI

Summary: Deep Learning models revolutionise data processing, solving complex image recognition, NLP, and analytics tasks. Introduction Deep Learning models transform how we approach complex problems, offering powerful tools to analyse and interpret vast amounts of data. billion in 2025 to USD 34.5

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Unveiling the GaoFen-7 Building Dataset: A New Horizon in Satellite-Based Urban and Rural Building Extraction

Marktechpost

In urban development and environmental studies, accurate and efficient building data extraction from satellite imagery is a cornerstone for myriad applications. These advanced methods grapple with a common Achilles’ heel: the dire need for extensive, high-quality training data reflective of real-world diversity.

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Most Important Deep Learning Interview Questions For You

Pickl AI

Summary: This guide covers the most important Deep Learning interview questions, including foundational concepts, advanced techniques, and scenario-based inquiries. Gain insights into neural networks, optimisation methods, and troubleshooting tips to excel in Deep Learning interviews and showcase your expertise.

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Deep Learning based Table Extraction using Visual NLP 1/2

John Snow Labs

Results for Image Table Detection using Visual NLP Introduction: Why is Table Extraction so crucial? Table recognition is a crucial aspect of OCR because it allows for structured data extraction from unstructured sources. The ImageTableDetector is a deep-learning model that identifies tables within images.

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Leveraging user-generated social media content with text-mining examples

IBM Journey to AI blog

Data extraction Once you’ve assigned numerical values, you will apply one or more text-mining techniques to the structured data to extract insights from social media data. And with advanced software like IBM Watson Assistant , social media data is more powerful than ever.

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Large Language Models in Pathology Diagnosis

John Snow Labs

The Impact of Data and Training Methodologies The effectiveness of Large Language Models (LLMs) in pathology hinges on the depth and breadth of datasets used for their training, which encompass a wide array of medical texts, pathology reports, and histopathological imagery. A notable study by Esteva et al.