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Digests » 114
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Whether you’re an old hand or you’re only paying attention to transformer style architecture for the first time, this article should offer something for you. First, we’ll dive deep into the fundamental concepts used to build the original 2017 Transformer. Then we’ll touch on some of the developments implemented in subsequent transformer models. Where appropriate we’ll point out some limitations and how modern models inheriting ideas from the original Transformer are trying to overcome various shortcomings or improve performance.
High-Resolution 3D Human Digitization from A Single Image.
In order to successfully understand and study new concepts or views, people generally use one example in their commonplace. They learn new skills much quicker and in more productive ways than machines; action, imagination, and explanation. For instance, children who have seen butterflies and ants a few times can promptly recognize them.
Deep learning’s recent history has been one of achievement: from triumphing over humans in the game of Go to world-leading performance in image recog- nition, voice recognition, translation, and other tasks. But this progress has come with a voracious appetite for computing power. This article reports on the computational demands of Deep Learning applications in five prominent application areas and shows that progress in all five is strongly reliant on in- creases in computing power. Extrapolating forward this reliance reveals that progress along current lines is rapidly becoming economically, technically, and environmentally unsustainable. Thus, continued progress in these applications will require dramatically more computationally-efficient methods, which will either have to come from changes to deep learning or from moving to other machine learning methods.
Ahh yeah its tetris, you might have heard of it. Tetris is a game which i am making, i have made it, thats what the video is, god what a terrible description. I create an AI to beat the world record for the longest tetris game.