英文字典中文字典


英文字典中文字典51ZiDian.com



中文字典辞典   英文字典 a   b   c   d   e   f   g   h   i   j   k   l   m   n   o   p   q   r   s   t   u   v   w   x   y   z       







请输入英文单字,中文词皆可:

epidermis    音标拼音: [,ɛpəd'ɚməs]
n. 表皮,上皮

表皮,上皮

epidermis
n 1: the outer layer of the skin covering the exterior body
surface of vertebrates [synonym: {epidermis}, {cuticle}]

Periostracum \Per`i*os"tra*cum\, n.; pl. {Periostraca}. [NL.,
fr. Gr. peri` around ? shell of a testacean.] (Zool.)
A chitinous membrane covering the exterior of many shells; --
called also {epidermis}.
[1913 Webster]


Epidermis \Ep`i*der"mis\, n. [L., fr. Gr. ?; ? over ? skin,
fr. ? to skin. See {Tear}, v. t.]
1. (Anat.) The outer, nonsensitive layer of the skin;
cuticle; scarfskin. See {Dermis}.
[1913 Webster]

2. (Bot.) The outermost layer of the cells, which covers both
surfaces of leaves, and also the surface of stems, when
they are first formed. As stems grow old this layer is
lost, and never replaced.
[1913 Webster]


请选择你想看的字典辞典:
单词字典翻译
Epidermis查看 Epidermis 在百度字典中的解释百度英翻中〔查看〕
Epidermis查看 Epidermis 在Google字典中的解释Google英翻中〔查看〕
Epidermis查看 Epidermis 在Yahoo字典中的解释Yahoo英翻中〔查看〕





安装中文字典英文字典查询工具!


中文字典英文字典工具:
选择颜色:
输入中英文单字

































































英文字典中文字典相关资料:


  • What is the difference between a convolutional neural network and a . . .
    A CNN, in specific, has one or more layers of convolution units A convolution unit receives its input from multiple units from the previous layer which together create a proximity Therefore, the input units (that form a small neighborhood) share their weights The convolution units (as well as pooling units) are especially beneficial as:
  • machine learning - What is a fully convolution network? - Artificial . . .
    A fully convolutional network is achieved by replacing the parameter-rich fully connected layers in standard CNN architectures by convolutional layers with $1 \times 1$ kernels I have two questions What is meant by parameter-rich? Is it called parameter rich because the fully connected layers pass on parameters without any kind of "spatial
  • What are the features get from a feature extraction using a CNN?
    By accessing these high-level features, you essentially have a more compact and meaningful representation of what the image represents (based always on the classes that the CNN has been trained on) By visualizing the activations of these layers we can take a look on what these high-level features look like
  • What is the fundamental difference between CNN and RNN?
    A CNN will learn to recognize patterns across space while RNN is useful for solving temporal data problems CNNs have become the go-to method for solving any image data challenge while RNN is used for ideal for text and speech analysis
  • Extract features with CNN and pass as sequence to RNN
    $\begingroup$ But if you have separate CNN to extract features, you can extract features for last 5 frames and then pass these features to RNN And then you do CNN part for 6th frame and you pass the features from 2,3,4,5,6 frames to RNN which is better The task I want to do is autonomous driving using sequences of images
  • In a CNN, does each new filter have different weights for each input . . .
    Typically for a CNN architecture, in a single filter as described by your number_of_filters parameter, there is one 2D kernel per input channel There are input_channels * number_of_filters sets of weights, each of which describe a convolution kernel So the diagrams showing one set of weights per input channel for each filter are correct
  • How to handle rectangular images in convolutional neural networks . . .
    Almost all the convolutional neural network architecture I have come across have a square input size of an image, like $32 \\times 32$, $64 \\times 64$ or $128 \\times 128$ Ideally, we might not have a
  • convolutional neural networks - When to use Multi-class CNN vs. one . . .
    I'm building an object detection model with convolutional neural networks (CNN) and I started to wonder when should one use either multi-class CNN or a single-class CNN That is, if I'm making e g a
  • deep learning - Artificial Intelligence Stack Exchange
    This is the same thing as in CNNs The only difference is that, in CNNs, the kernels are the learnable (or trainable) parameters, i e they change during training so that the overall loss (that the CNN is making) reduces (in the case CNNs are trained with gradient descent and back-propagation)





中文字典-英文字典  2005-2009