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Deriving a Score to Show Relative Socio-Economic Advantage and Disadvantage of a Geographic Area | by Jin Cui | Dec, 2023

There exist publicly accessible data which describe the socio-economic characteristics of a geographic location. In Australia where I reside, the Government through the Australian Bureau of Statistics (ABS) collects and publishes individual and household data on a regular basis in respect of income, occupation, education, employment and housing at an area level. Some examples of…

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How to Design a Batch Processing. Understand batch processing from… | by Xiaoxu Gao | Jan, 2024

Understand batch processing from business and technical perspective Photo by Dannie Sorum on UnsplashWe live in a world where every human interaction becomes an event in the system, whether it’s purchasing clothes online or in-store, scrolling social media, or taking an Uber. Unsurprisingly, all these events are processed in one way or the other.…

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Understanding Deep Learning Optimizers: Momentum, AdaGrad, RMSProp & Adam | by Vyacheslav Efimov | Dec, 2023

Gain intuition behind acceleration training techniques in neural networks D eep learning made a gigantic step in the world of artificial intelligence. At the current moment, neural networks outperform other types of algorithms on non-tabular data: images, videos, audio, etc. Deep learning models usually have a strong complexity and come up with millions or even…

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LLMs and Transformers from Scratch: the Decoder | by Luís Roque

Exploring the Transformer’s Decoder Architecture: Masked Multi-Head Attention, Encoder-Decoder Attention, and Practical Implementation This post was co-authored with Rafael Nardi. In this article, we delve into the decoder component of the transformer architecture, focusing on its differences and similarities with the encoder. The decoder’s unique feature is its loop-like, iterative nature, which contrasts with the…

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LLMs Are Dumber Than a House Cat. Can they replace you anyway? | by Nabil Alouani | Jan, 2024

Not to pick on Sebastian Bubeck in particular, but if auto-complete-on-steroid can “blow his mind,” imagine the effects on the average user. Developers and data practitioners use LLMs every day to generate code, synthetic data, and documentation. They too can be misled by inflated capabilities. It’s when humans over-trust their tools that mistakes happen. TL;DR:…

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