Hacker Noon - Transformers: Age of Attention

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🪐 What’s happening in tech today, August 27, 2024?

The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Compaq Introduced Presario in 1993, Mars approached closest to the Earth since 57,617 BC in 2003, NASA Launched Mariner 2 in 1962, and we present you with these top quality stories. From Transformers: Age of Attention to Expanding Design Exploration: Exploring Feature Spaces Beyond Parametric Boundaries, let’s dive right in.

Expanding Design Exploration: Exploring Feature Spaces Beyond Parametric Boundaries

TL;DR The project presented here marks a significant step forward in how designers can interact with complex datasets and uncover new possibilities in their work.

By @tomascbzn [ 6 Min read ]

The boundaries of design are constantly being pushed forward by the integration of cutting-edge technologies. As computational methods advance, designers are equipped with new tools that enhance their creative potential, allowing for unprecedented exploration of complex design spaces.

https://tcabezon.github.io/3Dexploration/?embedable=true..

Back to the Future: Running Spring Boot 2 in GlassFish 4 Using Java 8

TL;DR Learn how to deploy a Spring Boot 2.7.18 application on GlassFish 4.1.2 using Java 8, with step-by-step instructions and tips.

By @isharipov [ 8 Min read ]

Every developer dreams of working with modern technologies and staying up-to-date. In fact, it's nearly impossible to build a strong hiring strategy around outdated, obsolete, and often ineffective and even stillborn technologies.

However, life is complex, and not everything always depends on our desires.

Lambda Expressions

Stream API..

Transformers: Age of Attention

TL;DR Simple explanation of the Transformer model from the revolutionary paper Attention is All You Need which is the basis of many advanced AI systems.

By @bhavdeepsethi [ 7 Min read ]

The Key Idea: Attention To Architecture

Note that attention wasn't a new concept and was already being used in tandem with RNNs. Transformer was the first transduction model that relied solely on attention, thereby eliminating the need for neural networks. This gave the following advantages:..

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