KERAS 中文是什么意思 - 中文翻译

• keras

在 英语 中使用 Keras 的示例及其翻译为 中文

Deep learning and Keras.
深度学习和Keras.

We have used a Keras implementation of pointer networks.
我们使用了一个Keras执行的指针网络。

You have just found Keras.
你恰好发现了Keras。

Lastly, we let Keras print a summary of the model we have just built.
最后，我们让Keras打印我们刚刚构建的模型的摘要。

This signifies that the environment will have MXNet and Python 2(with Keras 1 and CUDA 9).
这意味着该环境具有MXNet和Python2(使用Keras1和CUDA9)。

Finally, let's have keras print a summary of the model we just built.
最后，我们让Keras打印我们刚刚构建的模型的摘要。

The AWS Deep Learning AMI comes pre-configured with popular frameworks such as Apache MXNet, TensorFlow, Caffe, and Keras.
AWS深度学习AMI自带预配置的流行框架，例如ApacheMXNet、TensorFlow、Caffe和Keras。

Keras is one of the most widely used deep learning frameworks for Python.
PyTorch是一个使用Python的常用深度学习框架。

Currently you can convert models that are trained with Keras, Caffe, scikit-learn, XGBoost, and libSVM.
目前，你可以转换使用Keras、Caffe、scikit-learn、XGBoost和libSVM训练的模型。

Inside this Keras tutorial, you will discover how easy it is to get started with deep learning and Python.
在这个Keras教程中，您将发现开始使用深度学习和Python是多么容易。

Machine learning-specific libraries such as SciKit-Learn, TensorFlow, Keras and others are also quite popular," the report said.
机器学习专用库，如SciKit-Learn，TensorFlow，Keras等也很受欢迎，”该报告称。

If you have tried Keras but you do not like it you can try these other libraries, maybe they're better for you.
如果你试过了Keras但是你不喜欢它你可以试试这些其他的库，也许它们更适合你。

His open source footprint includes contributions to many popular machine learning libraries, such as keras, deeplearning4j, and hyperopt.
他的开源足迹包括对许多流行的机器学习库的贡献，如keras、deeplearning4j和hyperopt。

Most importantly, you know that keras has made a great contribution to deep learning and the commercialization of artificial intelligence.
最重要的是，你了解到Keras对深度学习和人工智能的商业化做出了巨大贡献。

All of the machine learning and deep learning platforms like Tensorflow, PyTorch, Theano, and Keras are open source and have vibrant communities.
所有的机器学习和深度学习平台，如Tensorflow，PyTorch，Theano和Keras都是开源的，拥有充满活力的社区。

Keras is a Python deep learning library that can use the efficient Theano or TensorFlow symbolic math libraries as a backend.
Keras是一个Python深度学习库，它可以使用高效的Theano或TensorFlow符号数学库作为后端。

Some of the common ones are TensorFlow, Caffe, Keras, and Computational Network Toolkit(CNTK)[4, 5, 6, 7].
一些常见的框架有TensorFlow，Caffe，Keras和ComputationalNetworkToolkit(CNTK)[4，5，6，7]。

Keras is one of the most popular deep learning libraries, which has made great contribution to the commercialization of artificial intelligence.
Keras是目前最受欢迎的深度学习库之一，对人工智能的商业化做出了巨大贡献。

Google's TensorFlow is still the most popular deep learning platform at present, but the utilization rate of Keras is also very high, close to TensorFlow.
谷歌的TensorFlow仍然是是目前最受欢迎的深度学习平台，不过Keras的使用率也很高，接近TensorFlow。

Keras, one of the most popular and fastest-growing deep-learning frameworks, is widely recommended as the best tool to get started with deep learning.
Keras是最受欢迎且发展最快的深度学习框架之一，被广泛推荐为上手深度学习的最佳工具。

Using downloaded data from Yelp, you will learn how to install TensorFlow and Keras, train a deep learning language model and generate new restaurant reviews.
通过使用从Yelp下载的数据，您将了解如何安装TensorFlow和Keras、训练深度学习语言模型，并生成全新的餐厅评论。

Keras provides a convenient handler for importing the dataset which is also available as a serialized numpy array. npz file to download here.
Keras提供了一个方便的函数来导入该数据集，同时也可以在这里下载一个序列化的numpyarray.npy文件。

If you are considering learning one of these frameworks and have Python, numpy, pandas, sklearn, and matplotlib skills, I suggest you start with Keras.
如果你正考虑学习其中一种框架且具备Python、numpy、pandas、sklearn和matplotlib的技能，我建议你从Keras入手。

Keras is indeed more readable and concise, allowing you to build your first end-to-end deep learning models faster, while skipping the implementational details.
Keras确实更具可读性和简洁性，使你可以更快地构建自己的第一个端到端深度学习模型，同时跳过实现细节。

For data science and machine learning, developers typically use NumPy, Pandas, Matplotlib, with machine learning-specific libraries such as scikit-learn, TensorFlow and Keras also being popular.
对于数据科学和机器学习，开发人员通常使用NumPy、Pandas、Matplotlib，机器学习专用的库(如scikit-learn、TensorFlow和Keras)也很流行。

Code written for Keras, explained Basoglu, can now take advantage of the performance and speed available from the Cognitive Toolkit without requiring any code change.
Basoglu解释道，为Keras编写的代码现在可以利用CognitiveToolkit的性能与速度而无需改变任何代码。

The complete network, implemented using Keras, only contains 305,040 parameters and was trained for two weeks on a p3.2xlarge AWS machine using the Adam optimizer.
完整网络是用Keras实现的，仅包含305040个参数，并且使用Adam优化器在p3.2xlargeAWS机器上训练了两周时间。

Keras Functional API and Model Subclassing API: Allows for creation of complex topologies including using residual layers, custom multi-input/-output models, and imperatively written forward passes.
KerasFunctionalAPI和ModelSubclassingAPI：允许创建复杂的拓扑，包括使用剩余层，自定义多输入/输出模型以及强制写入的正向传递。

Importantly, Keras provides several model-building APIs(Sequential, Functional, and Subclassing), so you can choose the right level of abstraction for your project.
重要的是，Keras提供多个模型构建API(Sequential、Functional和Subclassing)，这样你可以选择适合自己项目的抽象级别。