hopefully this also inspires folks to add new text splitters! imo one of the most nuanced and interesting pieces, yet less discussed
MACHINE LEARNING
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H2O.ai Sponsors AI42023 with Open-Source ML Platform
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We're proud to be the official Open-Source ML Platform category sponsor at #Ai42023 Come by the http://
H2O.ai booth to learn how to leverage #GenAI to automate workflows while controlling your data and ensuring privacy. -
Fine-tuning Your Own Model Guide Released
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Check out the blog post to learn more about fine-tuning your own model. Happy hacking!
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Face Detection Training: Automatic Segmentation for Focused Model Learning
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Kick off your training. If you're training on faces, set the `use_face_detection_instead` to True. This will automatically use face segmentation so that training is focused only on the faces in your images.
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The Rise of the AI Engineer
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The Rise of the AI Engineer https://
bit.ly/45kX1Ho #AI #MachineLearning #DeepLearning #LLMs #DataScience -
Learn Data Science Skills: Insights from Industry Experts
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Love problem solving? There has never been a better time to learn data science skills! Hear from a few data scientists about their passion for their field, what drives them + the the future. How YOU can learn critical #DataScience skills. http://
2.sas.com/6014PbYUq -
Text-to-SQL Deep Dive: Steps and Implementation Guide
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A great deep dive by @manuelsoria_ and @RLanceMartin on text-to-SQL and all the steps involved!
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Understanding How Language Models Learn Beyond Training Data
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While large language models appear to have a rich understanding of the world, how do we know they’re not simply regurgitating from training data? Check out the latest AI Explorable on a phenomenon called grokking to learn more about how models learn. → https://t.co/Okc9GvJjuN pic.twitter.com/StxRLwtSRT
— Google AI (@GoogleAI) 8 août 2023While large language models appear to have a rich understanding of the world, how do we know they’re not simply regurgitating from training data? Check out the latest AI Explorable on a phenomenon called grokking to learn more about how models learn. → https://
goo.gle/45ohnQh -
Training Token Influence Across Neural Network Layers
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Influence can also be attributed to particular training tokens and network layers. On average, the influence is equally distributed over all layers (so the common heuristic of computing influence only over the output layer is likely to miss important generalization patterns).
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Neural Layer Influence Patterns: Fine-grained Wording vs Semantics
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On the other hand, individual influence queries show distinct influence patterns. The bottom and top layers seem to focus on fine-grained wording while middle layers reflect higher-level semantic information. (Here, rows correspond to layers and columns correspond to sequences.)