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相关概念视频

Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

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It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
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Cartesian Vector Notation01:28

Cartesian Vector Notation

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Cartesian vector notation is a valuable tool in mechanical engineering for representing vectors in three-dimensional space, performing vector operations such as determining the gradient, divergence, and curl, and expressing physical quantities such as the displacement, velocity, acceleration, and force. By using Cartesian vector notation, engineers can more easily analyze and solve problems in various areas of mechanical engineering, including dynamics, kinematics, and fluid mechanics. This...
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Cartesian Form for Vector Formulation01:26

Cartesian Form for Vector Formulation

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The Cartesian form for vector formulation is a process to calculate  the moment of force using the position and force vectors. The moment of force is defined as the cross-product of these vectors, making it a vector quantity. The Cartesian form of the position and force vectors involves unit vectors, which can be used to express the cross-product in determinant form.
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Vector Operations01:20

Vector Operations

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Vectors are physical quantities that have both magnitude and direction. The vector operations include addition, subtraction, and scalar multiplication.
A vector multiplied by a scalar value is called scalar multiplication. The result obtained is a new vector with a different magnitude. If the scalar is positive, the direction of the vector remains the same, but if it is negative, the direction of the vector is reversed. For example, the product of the mass and velocity yields the momentum.
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Vector Components in the Cartesian Coordinate System01:29

Vector Components in the Cartesian Coordinate System

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Vectors are usually described in terms of their components in a coordinate system. Even in everyday life, we naturally invoke the concept of orthogonal projections in a rectangular coordinate system. For example, if someone gives you directions for a particular location, you will be told to go a few km in a direction like east, west, north, or south, along with the angle in which you are supposed to move. In a rectangular (Cartesian) xy-coordinate system in a plane, a point in a plane is...
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Inertia Tensor01:24

Inertia Tensor

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The concept of the inertia tensor is employed to depict the mass distribution and rotational inertia of a solid or rigid object. This tensor is expressed through a three-by-three matrix. Each component within this matrix corresponds to varying moments of inertia about specific axes.
The diagonal components of the inertia tensor matrix represent the moments of inertia concerning the principal axes of the object. These primary axes are defined as the axes where the object experiences the least...
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相关实验视频

Updated: Jun 16, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

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变量张量神经网络用于深度学习.

Saeed S Jahromi1,2,3, Román Orús4,5,6

  • 1Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran.

Scientific reports
|August 16, 2024
PubMed
概括
此摘要是机器生成的。

我们通过将张量网络 (TN) 与深度神经网络 (NN) 集成,引入可扩展的张量神经网络 (TNN). 这种方法克服了可扩展性的局限性,使得深度学习模型的高效训练具有广泛的参数.

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 计算物理 计算物理

背景情况:

  • 深度神经网络 (NN) 面临着不断增加的神经元数量的可扩展性挑战,限制了网络深度.
  • 现有的NN架构与庞大的参数空间作斗争,阻碍了复杂任务的性能.

研究的目的:

  • 开发一个可扩展的神经网络架构,克服深度和参数限制.
  • 通过将张量网络 (TN) 集成到 NN 框架中,引入一种新的张量神经网络 (TNN).
  • 为了实现深度学习模型的高效训练,具有大量的参数.

主要方法:

  • 在神经网络 (NN) 架构中集成张量网络 (TN).
  • 为TNNs开发一种由DMRG启发的变异性训练技术.
  • 使用局部梯度下降方法进行张量梯度计算,允许混合密度和张量层.

主要成果:

  • 展示了一个可扩展的张量神经网络 (TNN) 架构.
  • 在一个大的参数空间上实现了高效的训练.
  • 提供了回归,分类和图像识别 (MNIST) 的基准结果,以验证TNN的准确性和效率.

结论:

  • 提议的TNN架构有效地解决了深度学习中的可扩展性限制.
  • 变化训练算法能够有效处理大型参数空间,并提供对模型纠的见解.
  • 超级神经网络为开发更深入,更高效的神经网络提供了一个有希望的方向.