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evjang.com | ||
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blog.evjang.com
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| | | | | Translated versions of this blog post: Español , ?? . I'm still looking for translators to help translate this post into different langua... | |
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dennybritz.com
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| | | | | Deep Learning is such a fast-moving field and the huge number of research papers and ideas can be overwhelming. | |
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clemenswinter.com
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| | | | | I spent a good chunk of my time over the last two years applying deep reinforcement learning techniques to create an AI that can play the CodeCraft real-time strategy game. My primary motivation was to learn how to tackle nontrivial problems with machine learning and become proficient with modern auto-differentiation frameworks. Thousands of experiment runs... | |
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jalammar.github.io
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| | | Discussions: Hacker News (397 points, 97 comments), Reddit r/MachineLearning (247 points, 27 comments) Translations: German, Korean, Chinese (Simplified), Russian, Turkish The tech world is abuzz with GPT3 hype. Massive language models (like GPT3) are starting to surprise us with their abilities. While not yet completely reliable for most businesses to put in front of their customers, these models are showing sparks of cleverness that are sure to accelerate the march of automation and the possibilities of intelligent computer systems. Let's remove the aura of mystery around GPT3 and learn how it's trained and how it works. A trained language model generates text. We can optionally pass it some text as input, which influences its output. The output is generated from what the model "learned" during its training period where it scanned vast amounts of text. | ||
