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Five Data-Loading Patterns To Improve Frontend Performance

Smashing Magazine

Five Data-Loading Patterns To Improve Frontend Performance. Five Data-Loading Patterns To Improve Frontend Performance. When it comes to performance, you shouldn’t be stingy. The resource loading waterfall is a cascade of files downloaded from the network server to the client to load your website from start to finish.

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Improving JavaScript Bundle Performance With Code-Splitting

Smashing Magazine

Improving JavaScript Bundle Performance With Code-Splitting. Improving JavaScript Bundle Performance With Code-Splitting. This problem is more apparent on unreliable and slow networks and lower-end devices. Jump to online workshops ?. Performance Costs. Adrian Bece. 2022-02-02T10:30:00+00:00. More after jump!

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Exploring The Potential Of Web Workers For Multithreading On The Web

Smashing Magazine

They were designed to provide a way to execute JavaScript code in the background, separate from the main execution thread of a web page, in order to improve performance and responsiveness. Web Workers are designed to perform tasks that do not require direct access to the UI, such as data processing, image manipulation, or calculations.

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Meet Hydrogen: A React Framework For Dynamic, Contextual And Personalized E-Commerce

Smashing Magazine

We work with millions of merchants, and as many companies, we obsess about storefront performance. We obsess even more about finding the optimal balance between performance, capabilities, and merchants’ business objectives. Jump to the workshop ?. (vf, From complex mega-dropdowns and tables to web forms, carousels and filters.

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Architect your software delivery value stream for speed and quality with integration patterns

Tasktop

The complex — and often implicit — network of teams, tools and processes that plan, build and deliver are disconnected by the role-specific tools used by teams across the ideate, create, release and operate stages. . This two-day hands-on workshop shows you how to enable flow in your organization using lean practices. Learn more.

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A Brief Guide of xPU for AI Accelerators

ACM Sigarch

TFLOPS deep learning performance, and is equipped with 32GB of high-bandwidth memory (HBM) capacity. The new DGX-2, which achieves 2 petaFLOPS in the system, combines 16 fully interconnected GPUS with 10x the deep learning performance. TPU: Tensor Processing Unit (TPU) is Google’s specialized hardware for neural network.

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Snuba: automating weak supervision to label training data

The Morning Paper

F1 points in terms of end model performance. Allowing heuristics to abstain from labelling datapoints where they have low confidence is a key part of how Snuba achieves its final performance. The generative model assumes that each heuristic performs better than random. It took me quite a while to get my head around this!