There is a huge amount of information that the convolutional neural network has to make sense of.

这一堆海量信息需要积神经网来处理。

The authors of the paper invoke a classical learning algorithm, a convolutional neural network to accomplish that.

作者引用了一种经典学习算法，即使用一个积神经网。

This work uses a 3D convolutional neural network to accomplish this task.

研究中使用了三维积神经网。

This is typically achieved by a classical machine learning technique that we call a convolutional neural network.

这通常是用一种经典名为积神经网器学习术完成。

Today, the hot new algorithms on the block are Convolutional Neural Networks.

如今，最火新算法是积神经网。

This should sound vaguely familiar, because it's a lot like a convolution.

这应该听起来有点熟悉，因为它很像一次积。

It's similar to the story of RNNs and CNNs, of RNNs and CovNets.

这与RNN（循环神经网）CNN（积神经网）故事类似，即RNN与CovNets对比。

Machine learning kernels such as LSTMs, convolutions, and neuron layers are included as well as an expanded set of matrix math operations.

器学习内核，比如 LSTMs、积码神经元也包括在内，还有扩展矩阵数学操作。

However, this is not your classical Convolutional Neural Network, because as we mentioned, the input is 2D and the output is 3D.

然而，这并不是传统积神经网算法，因为正如我们曾提到，它输入是2D，输出是3D。

For example, convolutional neural networks have been used to extract features and classify drone images, achieving good recognition results.

例如，积神经网已被用于对无人图像进行特征提取分类，取得了良好识别效果。

Deep learning models, such as convolutional neural networks (CNN), have shown great capabilities in processing image data.

深度学习模型， 例如积神经网（CNN），在处理图像数据方面表现出了强大能力。

The results show that ResNet101 performed better than VGG16 (Visual Geometry Group 16) convolutional neural network.

结果表明，ResNet101表现优于VGG16（视觉几何组16层）积神经网。

Sometimes when  students see the definition of a convolution out of context, it can seem a little intimidating.

有时当学生在没有上下文情况下看到积定义时，可能会觉得有点吓人。

In image data, long-distance dependencies are typically modeled by the large receptive fields formed through deep stacks of convolutional operations.

在图像数据中， 长距离依赖性通常由通过深度积运算堆栈形成大感受野建模。

There's some added steps with carries and the like but the core step is a convolution.

有一些额外进位步骤之类，但核心步骤是积。

These outputs are then processed by subsequent layers of neurons, allowing for convolutions on convolutions on convolutions.

这些输出随后由后续神经元层处理，从而实现积积积。

The loss function and the anchors in the RPN of the convolutional neural network were optimized.

积神经网中区域提议网（RPN）内损失函数锚点得到了优化。

But not this time, because here, super effective descriptors are proposed automatically via a learning algorithm, a convolutional neural network and siamese networks.

但是这次不一样了，因为通过一种学习算法、积神经网 siamese 网，而自动生成了超级有效描述符。

They did this using something called a deep convolutional neural network and trained it with a dataset containing about 130,000 images.

他们使用一种叫做深度积神经网东西来做到这一点，并用一个包含大约130,000张图像数据集来训练它。

Moreover, separable deep convolution blocks were employed to enhance object feature information and reduce model computation.

此外，采用了可分离深度积块，以增强对象特征信息并减少模型计算量。