在 中文 中使用 矩阵乘法 的示例及其翻译为 英语
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这两种运算本质上都是矩阵乘法。
Both of these operations are essentially matrix multiplications.这两种运算本质上都是矩阵乘法。
Both operations are essentially matrix multiplications.矩阵乘法受益于二维硬件。
Matrix multiplies benefit from two-dimensional hardware;矩阵乘法中的卷积的实现遵循CxLarge=Small。
The implementation of convolution in matrix multiplication follows as C x Large= Small.矩阵乘法中的卷积实现遵循CxLarge=Small。
The implementation of convolution in matrix multiplication follows as C x Large= Small.Combinations with other parts of speech
用名词使用
事实上，卷积运算还可以通过矩阵乘法来实现。
In fact,convolution operations can also be achieved by matrix multiplication.GPU执行矩阵乘法必须比传统CPU更快，这意味着机器学习和深度学习模型可以执行得更快。
GPUs perform matrix multiplication must faster than traditional CPUs, which means machine learning and deep learning models can perform much faster.矩阵乘法单元下的16位产品被收集在32位累加器的4MiB中。
The 16-bit products of the Matrix Multiply Unit are collected in the 4 MiB of 32-bit Accumulators below the 矩阵乘法是一个很有前途的领域，它利用了量子计算机分析这么多信息的技术。
One promising field is matrix multiplication, which takes advantage of the techniques that allow quantum computers to be able to analyze so much information.卷积的矩阵乘法：将Small输入图像（2×2）转换为Large输出图像（4×4）.
Matrix multiplication for convolution: from a Small input image(2 x 2) to a Large output image(4 x 4).答案当然是肯定的，我们可以使用矩阵乘法来更简单的实现这一点。
The answer is that we can use matrix multiplications to do this more simply.因此占位符（数据）和变量（权重和偏置项）需要组合成一个连续的矩阵乘法系统。
Hereby, placeholders(data) and variables(weights and biases)need to be combined into a system of sequential matrix multiplications.她说，在矩阵乘法方面，她的工作现在有了轻微的转变，显示现有的技术“不能做得更好”。
In matrix multiplication, her work has now shifted slightly to showing that existing techniques"cannot do better," she says.一个比较好的起始点是矩阵乘法，就像上面一样，它是实现线性回归算法的很好的方法。
A good place to start is matrix multiplication, as treated above, which is a well-used method for implementing linear regression algorithms.这意味着，无论我们重复多少次矩阵乘法，矩阵的结果既不会爆炸也不会消失。
This means that no matter how many times we perform repeated matrix multiplication, the resulting 我们之前讨论过矩阵乘法不是可交换的，但是有一个例外，即如果我们将矩阵乘以单位矩阵。
We previously discussed that Matrix multiplication is not commutative but there is one exception, namely if we multiply a 但是某些矩阵的群是在矩阵乘法下的阿贝尔群-一个例子是2x2旋转矩阵的群。
However, some groups of 在矩阵乘法，她的工作现在已经略微转向显示，现有的技术“不能做的更好，”她说。
In matrix multiplication, her work has now shifted slightly to showing that existing techniques"cannot do better," she says.矩阵即使是可逆矩阵，一般不形成在乘法下的阿贝尔群，因为矩阵乘法一般是不可交换的。
In general, 在实践中，表达式同样很有用，因为大多数矩阵库提供了实现矩阵乘法、向量加法和向量化的快速方法。
The expression is also useful in practice,because most 示例1：当a和b是矩阵(2阶)时,axes=1相当于矩阵乘法.
Example 1: When a and b are matrices(order 2),the case axes= 1 is equivalent to matrix multiplication.为了说明如何在实践中使用WebAssembly，我将解释如何在WebAssembly中实现Quicksort和矩阵乘法。
To show how WebAssembly can be used in practice,I will explain how to implement Quicksort and matrix multiplication in WebAssembly.最近的发展包括格式化的字符串，类创建的简单定制，和用一种更干净的句法方式来处理矩阵乘法。
Recent developments have included formatted string literals, simpler customization of class creation,and a cleaner syntactical way to handle matrix multiplication.在卷积层中，我们的权重仍然是矩阵的元素，但是他们不再通过矩阵乘法对整个输入进行变换。
In a convolutional layer, our weights are still elements of 正如Wierzynski所指出的那样，除了矩阵乘法之外，神经网络还有第二个基本特征：非线性。
As Wierzynski noted, neural networks have a second basic building block,in addition to matrix multiplications: nonlinearities.矩阵乘法中的卷积完结遵从CxLarge=Small。
The implementation of convolution in matrix multiplication follows as C x Large= Small.在矩阵乘法中，将第一矩阵的一个行元素与第二矩阵的所有列元素相乘。
In case of matrix multiplication, one row element 例如，使用矩阵乘法，两个矩阵的大小必须以特定方式匹配。
For example, with matrix multiplication the sizes of the two 你知道其中会有大量矩阵乘法运算，但你不知道神经元权重的规格。
You know there will be a high number of matrix multiplies, but you don't know the specs for the neuron weights.这种矩阵乘法的卷积的实现遵照：CxLarge=Small。
The implementation of convolution in matrix multiplication follows as C x Large= Small.
