Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs01:21

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

1.1K
The fundamental mathematical principles, such as calculus and graphs, play crucial roles in analyzing drug movement and determining pharmacokinetic parameters. Differential calculus examines rates of change and helps to determine the dissolution rate of drugs in biofluids, as well as how drug concentrations change over time. For instance, it can help calculate the rate of elimination of a drug from the body based on its concentration-time profile.
On the other hand, integral calculus focuses on...
1.1K
Fundamental Mathematical Principles in Pharmacokinetics: Mathematical Expressions and Units01:19

Fundamental Mathematical Principles in Pharmacokinetics: Mathematical Expressions and Units

543
Mathematical principles play a crucial role in pharmacokinetics, providing a framework for understanding and quantifying drug distribution and elimination dynamics in the body. By utilizing mathematical expressions and units, pharmacologists can accurately characterize the behavior of drugs, optimize dosing regimens, and predict therapeutic outcomes.
One significant application of mathematics in pharmacokinetics is the characterization of drug distribution through the volume of distribution...
543
Dimensional Analysis02:19

Dimensional Analysis

14.9K
The concept of dimension is important because every mathematical equation linking physical quantities must be dimensionally consistent, implying that mathematical equations must meet the following two rules. The first rule is that, in an equation, the expressions on each side of the equal sign must have the same dimensions. This is fairly intuitive since we can only add or subtract quantities of the same type (dimension). The second rule states that, in an equation, the arguments of any of the...
14.9K
Divergence and Stokes' Theorems01:06

Divergence and Stokes' Theorems

1.5K
The divergence and Stokes' theorems are a variation of Green's theorem in a higher dimension. They are also a generalization of the fundamental theorem of calculus. The divergence theorem and Stokes' theorem are in a way similar to each other; The divergence theorem relates to the dot product of a vector, while Stokes' theorem relates to the curl of a vector. Many applications in physics and engineering make use of the divergence and Stokes' theorems, enabling us to write...
1.5K
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

231
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
231
Geometric Mean01:15

Geometric Mean

3.4K
The mean is a measure of the central tendency of a data set. In some data sets, the data is inherently multiplicative, and the arithmetic mean is not useful. For example, the human population multiplies with time, and so does the credit amount of financial investment, as the interest compounds over successive time intervals.
In cases of multiplicative data, the geometric mean is used for statistical analysis. First, the product of all the elements is taken. Then, if there are n elements in the...
3.4K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Semiparametric accelerated failure time models with time-varying covariates under partly interval censoring.

BMC medical research methodology·2026
Same author

Hydrocortisone versus dexamethasone in cerebral salt-wasting after aneurysmal subarachnoid hemorrhage.

Brain & spine·2026
Same author

Enhancing logistic regression classification: insights from simulation and real-world applications through ranked set sampling.

Scientific reports·2026
Same author

Meta-analysis models with group structure for pleiotropy detection at gene and variant level using summary statistics from multiple datasets.

Biostatistics (Oxford, England)·2025
Same author

Nonstationary Spatial Process Models with Spatially Varying Covariance Kernels.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2025
Same author

Best Subset Solution Path for Linear Dimension Reduction Models Using Continuous Optimization.

Biometrical journal. Biometrische Zeitschrift·2024

相关实验视频

Updated: Jun 7, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K

导航数学基础:科学深度学习的入门书

Benoit Liquet1,2, Sarat Moka3, Yoni Nazarathy4,5

  • 1School of Mathematical and Physical Sciences, Macquarie University, Macquarie Park, NSW, Australia. benoit.liquet-weiland@mq.edu.au.

Advances in experimental medicine and biology
|November 10, 2024
PubMed
概括

这个数学速览课程介绍了对于理解深度学习算法至关重要的基本数学符号. 它使科学家能够在没有广泛的先前数学知识的情况下掌握机器学习中的复杂公式和模型.

关键词:
深度学习 (Deep Learning) 是一种深度学习.机器学习是机器学习.数学用于数据科学 数学用于数据科学机器学习的数学

更多相关视频

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.3K

相关实验视频

Last Updated: Jun 7, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.3K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 深度学习模型严重依赖于数学符号.
  • 没有强大的数学背景的科学家面临着理解这些模型的挑战.
  • 现有的资源可能无法提供一个温柔的,特定于背景的介绍.

研究的目的:

  • 为深度学习提供对初级数学符号的轻松介绍.
  • 为了使科学家能够理解深度学习方程和算法的构建块.
  • 帮助非数学读者克服阅读技术文本的障碍.

主要方法:

  • 不正式介绍数学概念,如总结,集合和函数.
  • 在深度学习的背景下解释矢量,矩阵和梯度.
  • 使用sigmoid和softmax等基本深度学习模型进行演示.

主要成果:

  • 在深度学习中使用的常见数学符号的解密.
  • 为更广泛的科学受众提高了深度学习文献的可访问性.
  • 了解更复杂的神经网络架构的基础.

结论:

  • 了解基本的数学符号是理解深度学习原理的关键.
  • 这种资源可以作为科学家参与机器学习的桥梁.
  • 简化数学解释有助于采用深度学习技术.