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Tag: overfitting

  • Hacker News: Writing an LLM from scratch, part 10 – dropout

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    Kurt Seifried
    in

    Source URL: https://www.gilesthomas.com/2025/03/llm-from-scratch-10-dropout Source: Hacker News Title: Writing an LLM from scratch, part 10 – dropout Feedly Summary: Comments AI Summary and Description: Yes Summary: The text details the concept and implementation of dropout within the training of large language models (LLMs), specifically within a PyTorch context. It illustrates the importance of dropout in spreading…

  • Hacker News: Evaluating Code Embedding Models

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    Kurt Seifried
    in

    Source URL: https://blog.voyageai.com/2024/12/04/code-retrieval-eval/ Source: Hacker News Title: Evaluating Code Embedding Models Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses the challenges and limitations within the field of code retrieval, particularly as it pertains to embedding models used in coding assistants. It highlights the need for high-quality benchmarking datasets, identifies typical subtasks…

  • Cloud Blog: Supervised Fine Tuning for Gemini: A best practices guide

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/master-gemini-sft/ Source: Cloud Blog Title: Supervised Fine Tuning for Gemini: A best practices guide Feedly Summary: Foundation models such as Gemini have revolutionized how we work, but sometimes they need guidance to excel at specific business tasks. Perhaps their answers are too long, or their summaries miss the mark. That’s where supervised fine-tuning…

  • Hacker News: Bayesian Neural Networks

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    system automation
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    Source URL: https://www.cs.toronto.edu/~duvenaud/distill_bayes_net/public/ Source: Hacker News Title: Bayesian Neural Networks Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses Bayesian Neural Networks (BNNs) and their ability to mitigate overfitting and provide uncertainty estimates in predictions. It contrasts standard neural networks, which are flexible yet prone to overfitting, with BNNs that utilize Bayesian…