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blog.ml.cmu.edu | ||
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haifengl.wordpress.com
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| | | | | Generative artificial intelligence (GenAI), especially ChatGPT, captures everyone's attention. The transformerbased large language models (LLMs), trained on a vast quantity of unlabeled data at scale, demonstrate the ability to generalize to many different tasks. To understand why LLMs are so powerful, we will deep dive into how they work in this post. LLM Evolutionary Tree... | |
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www.assemblyai.com
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| | | | | Learn how ChatGPT works under the hood in this easy-to-follow guide. | |
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neptune.ai
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| | | | | You can apply the key ideas of this "Google Collab-friendly" approach to many other base models and tasks. | |
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www.asimovinstitute.org
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| | | With new neural networkarchitectures popping up every now and then, its hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containingmany of thosearchitectures. Most of theseare neural networks, some are completely [] | ||
