Remove category application-development
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Transformer models: A guide to understanding different transformer architectures and their uses

Data Science Dojo

Their role is critical to ensure improved accuracy, faster training on data, and wider applicability. The very common model types under this category include encoder-only, decoder-only, and encoder-decoder transformers. This knowledge enables MLM models to contribute and excel in diverse NLP applications.

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Unlocking the Power of Containers: Exploring the Top 20 Docker Containers for Every Development Need

Analytics Vidhya

Introduction Docker containers have emerged as indispensable tools in the fast-evolving landscape of software development and deployment, providing a lightweight and efficient way to package, distribute, and run applications.

Analytics 282
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Rust for Embedded Systems: Current state, challenges and open problems

Hacker News

Most embedded codebases are developed in unsafe languages, specifically C/C++, and are riddled with memory safety vulnerabilities. To prevent such vulnerabilities, RUST, a performant memory-safe systems language, provides an optimal choice for developing embedded software. Our study is organized across three research questions.

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The future of application delivery starts with modernization

IBM Journey to AI blog

Where and how these applications are deployed will impact time to market and value realization. The reality is that application landscapes are complex, and they challenge enterprises to maintain and modernize existing infrastructure, while delivering new cloud-native features. How efficient is your release process?

ML 96
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New Study: 2018 State of Embedded Analytics Report

We surveyed 500+ application teams embedding analytics to find out which analytics features actually move the needle. Why do some embedded analytics projects succeed while others fail? Read the 6th annual State of Embedded Analytics Report to discover new best practices. Brought to you by Logi Analytics.

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The rise of machine learning applications in healthcare

Dataconomy

Machine learning applications in healthcare are rapidly advancing, transforming the way medical professionals diagnose, treat, and prevent diseases. Let’s discover some notable applications in this area. What is machine learning? From personalized medicine to disease prevention, the possibilities are endless.

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What are Small Language Models (SLMs) and how do they work?

Dataconomy

Crafted to be more lightweight and resource-conserving, SLMs are perfect for applications that must function within constrained computational settings. Generally, researchers agree that language models with fewer than 100 million parameters fall under the “small” category, although this classification can differ.