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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Case Studies01:22

Case Studies

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There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it.
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Language and Cognition01:27

Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Modeling in Therapy01:26

Modeling in Therapy

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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
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相关实验视频

Updated: May 15, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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微调大型语言模型,用于专门的用例.

D M Anisuzzaman1, Jeffrey G Malins1, Paul A Friedman1

  • 1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.

Mayo Clinic proceedings. Digital health
|April 10, 2025
PubMed
概括

微调大型语言模型 (LLM) 将人工智能适应专门任务. 本综述探讨了方法,步骤和医疗用例,讨论了人工智能在医疗保健中的好处和局限性.

科学领域:

  • 人工智能的人工智能
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 大型语言模型 (LLM) 展示了理解和生成类似人类文本的高级能力.
  • 通过LLM支持ChatGPT和Claude等应用程序,改变了人与计算机之间的互动.
  • 微调将预训练的LLM适应特定领域,使用自定义数据集.

研究的目的:

  • 审查用于微调LLMs的主要方法方法.
  • 概述LLM微调所涉及的一般步骤.
  • 介绍医学子专业中微调的LLM的使用案例.

主要方法:

  • 关于LLM微调技术的现有文献的审查.
  • 专门的LLM适应方法的方法方法的分类.
  • 对医学中微调应用的案例研究分析.

主要成果:

  • 识别关键技术,以适应LLMs的专业任务.
  • 对实施LLM微调的结构化流程的描述.
  • 在各种医学领域对LLM微调的说明性示例.

结论:

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  • 微调提供了一种强大的方式,可以为特定应用程序 (包括医学) 定制LLM.
  • 了解好处和局限性对于负责任的实施至关重要.
  • 专业的LLM具有促进医学研究和实践的巨大潜力.