What is the translation of " 我们的神经网络 " in English?

• our neural network
• our neural networks

Examples of using 我们的神经网络 in Chinese and their translations into English

这使我们的神经网络学习普遍化。
This allows our neural network to learn to generalize.

我们的神经网络将会因此变得更好。
Our neural networks will be the better for it.

我们的神经网络总共有6000万个参数。
Our neural network architecture has 60 million parameters.

我们的神经网络在预测故事能否流行方面取得了一些成功。
Our neural networks had some success in predicting the popularity of stories.

但是现在，我们的神经网络还做不到这一点。
But right now, our neural network can't do this.

我们需要一种方法让Supervisor自动启动我们的神经网络。
We need a way for our Supervisor to start up our Neural Network automatically.

这个新公式以几种方式改变了我们的神经网络。
Our neural network is altered in various ways by this new formulation.

我们的神经网络应该学习理想的权重集来表示这个函数。
Our Neural Network should learn the ideal set of weights to represent this function.

虽然我们的神经网络给出了令人印象深刻的表现，但是这个过程有一些神秘。
While our neural network gives impressive performance, that performance is somewhat mysterious.

我们专门培训了我们的神经网络，令其产生任何设计师都可以轻松进行交互和操作的图层。
We trained our neural networks specifically to produce these layers in a way that any designer can easily interact with and manipulate.

我们的神经网络应该能够习得理想的权重集合以表示这个函数。
Our Neural Network should learn the ideal set of weights to represent this function.

冰岛可再生能源丰富，因此我们可以在这里用非常低廉的成本训练我们的神经网络。
Due to the abundance of renewable energy, we can train our neural networks very cost-efficiently in Iceland.

虽然我们的神经网络给出了令人印象深刻的表现，但这个过程有一些神秘。
While our neural network gives impressive performance, that performance is somewhat mysterious.

在课程的第一部分中，我们将添加时间概念到我们的神经网络。
In the first section of the course we are going to add the concept of time to our neural networks.

我们的神经网络应该学习到立项的权重集来展示这个函数。
Our Neural Network should learn the ideal set of weights to represent this function.

现在我们已经完成了预处理和分割我们的数据集，我们可以开始实施我们的神经网络。
Now that we have obtained data both for pre- and fine-tuning, we can move on and start training our neural networks.

现在我们已经完成了预处理和分割我们的数据集，我们可以开始实施我们的神经网络。
Now that we're done pre-processing and splitting our dataset we can start implementing our neural network.

现在我们需要创建我们的输入和输出键值对来训练我们的神经网络。
Now we need to create our input and output pairs on which to train our neural network.

我们的神经网络正在预测我说的那个词很有可能是「HHHEE_LL_LLLOOO」。
Our neural net is predicting that one likely thing I said was“HHHEE_LL_LLLOOO”.

这个约束将强加我们的神经网络来学习压缩的数据表示。
This constraint will impose our neural net to learn a compressed representation of data.

尽管还很初期，但我们的神经网络已经显示出了高度的准确性，它的准确度达到了89%，而人类医生只有73%。
It's early days but our neural nets show a much higher degree of accuracy: 89 percent, compared to 73 percent.

我们这样做是因为大多数的评论长度差不多都在这个长度，并且我们的神经网络的每次输入都需要具有相同的大小。
We do this because the biggest review is nearly that long and every input for our neural network needs to have the same size.

同样是因为没有激活函数，我们的神经网络将无法学习和模拟其他复杂类型的数据，例如图像、视频、音频、语音等。
Also without activation function our neural networks would not be able to learn and other kinds of data such as images, videos, audio, speech etc.

我们这样做是因为大多数的评论长度差不多都在这个长度，并且我们的神经网络的每次输入都需要具有相同的大小。
We do this because most of the comments are about this length, and each input of our neural network needs to be the same size.

但是，我的神经网络的输入大小是固定的，所以我需要做一些处理工作。
My neural network will take a fixed-size input, so I have some preprocessing to do.

我们来定义我们的神经网络架构。
We now define our Neural Network model.

我们的神经网络结构有6000万参数。
Our neural network architecture has 60 million parameters.

我们的神经网络架构有6000万参数。
Our neural network architecture has 60 million parameters.

接下来我们将讨论我们的神经网络架构。
Next, we will construct our neural network.

我们的神经网络被表示为ElixirStruct。
Our Network is listed as an Elixir Struct.