chore(i18n,chn): manually downloaded curriculum (#42858)
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---
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id: 5e9a0e9ef99a403d019610cc
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title: Deep Learning Demystified
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title: 解密深度学习
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challengeType: 11
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videoId: bejQ-W9BGJg
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dashedName: deep-learning-demystified
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@ -10,23 +10,23 @@ dashedName: deep-learning-demystified
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## --text--
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How should you assign weights to input neurons before training your network for the first time?
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在你第一次训练你的网络之前，你应该如何给输入层节点分配权重？
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## --answers--
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From smallest to largest.
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从小到大
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---
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Completely randomly.
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完全随机的
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---
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Alphabetically.
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按字母顺序
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---
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None of the above.
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以上都不对
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## --video-solution--
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---
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id: 5e9a0e9ef99a403d019610cd
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title: How Convolutional Neural Networks work
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title: 卷积神经网络的工作原理
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challengeType: 11
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videoId: Y5M7KH4A4n4
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dashedName: how-convolutional-neural-networks-work
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@ -10,19 +10,19 @@ dashedName: how-convolutional-neural-networks-work
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## --text--
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When are Convolutional Neural Networks not useful?
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卷积神经网络在什么时候是没有用的？
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## --answers--
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If your data can't be made to look like an image, or if you can rearrange elements of your data and it's still just as useful.
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当数据的组成形式不能像图片存储的数据格式一样，或者说你的数据可以重新排列，它仍然可以被运用到卷积神经网络中。
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---
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If your data is made up of different 2D or 3D images.
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如果你的数据是由 2D 或者 3D 图片组成的。
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---
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If your data is text or sound based.
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如果你的数据是基于文本或者音频的形式。
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## --video-solution--
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---
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id: 5e9a0e9ef99a403d019610ca
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title: How Deep Neural Networks Work
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title: 深度神经网络的工作原理
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challengeType: 11
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videoId: zvalnHWGtx4
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dashedName: how-deep-neural-networks-work
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## --text--
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Why is it better to calculate the gradient (slope) directly rather than numerically?
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相比较数字的计算，为什么深度神经网络可以更好地计算梯度（斜率）？
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## --answers--
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It is computationally expensive to go back through the entire neural network and adjust the weights for each layer of the neural network.
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通过回溯整个神经网络来更改每一层神经网络的权重，在计算上来说是非常耗时的。
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---
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It is more accurate.
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它更加准确。
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---
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There is no difference between the two methods.
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这两种方法之间没有区别。
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## --video-solution--
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---
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id: 5e9a0e9ef99a403d019610cb
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title: Recurrent Neural Networks RNN and Long Short Term Memory LSTM
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title: 循环神经网络 RNN 和长短期记忆 LSTM
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challengeType: 11
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videoId: UVimlsy9eW0
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dashedName: recurrent-neural-networks-rnn-and-long-short-term-memory-lstm
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## --text--
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What are the main neural network components that make up a Long Short Term Memory network?
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构成长短期记忆网络的主要神经网络组件是什么？
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## --answers--
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New information and prediction.
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新的信息和预测
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---
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Prediction, collected possibilities, and selection.
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预测、收集的可能性和选择
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---
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Prediction, ignoring, forgetting, and selection.
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预测、忽视、遗忘和选择
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## --video-solution--
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---
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id: 5e46f8e3ac417301a38fb92f
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title: Book Recommendation Engine using KNN
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title: 基于 KNN 的图书推荐引擎
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challengeType: 10
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forumTopicId: 462378
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dashedName: book-recommendation-engine-using-knn
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---
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# --description--
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In this challenge, you will create a book recommendation algorithm using K-Nearest Neighbors.
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在这个挑战中，你将使用 K-近邻算法创建一个书籍推荐算法。
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You will use the Book-Crossings dataset. This dataset contains 1.1 million ratings (scale of 1-10) of 270,000 books by 90,000 users.
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你将使用 Book-Crossings 数据集。 该数据集包括 90,000 名用户对 270,000 册书籍的 110 万份评分（评分从 1至 10）。
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You can access [the full project instructions and starter code on Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-book-recommendation-engine/blob/master/fcc_book_recommendation_knn.ipynb).
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你可以[在 Replit 上查看整个项目的具体描述和初始代码](https://colab.research.google.com/github/freeCodeCamp/boilerplate-book-recommendation-engine/blob/master/fcc_book_recommendation_knn.ipynb)。
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After going to that link, create a copy of the notebook either in your own account or locally. Once you complete the project and it passes the test (included at that link), submit your project link below. If you are submitting a Google Colaboratory link, make sure to turn on link sharing for "anyone with the link."
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进入该链接后，在你自己的账户或本地创建一个笔记本的副本。 一旦你完成项目并通过链接中的测试，请在下面提交你的项目链接。 如果你提交的是 Google Colaboratory 的链接，请确保打开链接共享时选择 “anyone with the link”。
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We are still developing the interactive instructional content for the machine learning curriculum. For now, you can go through the video challenges in this certification. You may also have to seek out additional learning resources, similar to what you would do when working on a real-world project.
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我们仍在开发机器学习课程的交互式课程部分。 现在，你可以通过这个认证视频挑战。 你可能还需要寻找额外的学习资源，类似于你在现实世界项目上所做的工作。
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# --hints--
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It should pass all Python tests.
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它应该通过所有的 Python 测试。
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```js
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---
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id: 5e46f8dcac417301a38fb92e
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title: Cat and Dog Image Classifier
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title: 猫狗图像分类器
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challengeType: 10
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forumTopicId: 462377
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dashedName: cat-and-dog-image-classifier
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---
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# --description--
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For this challenge, you will use TensorFlow 2.0 and Keras to create a convolutional neural network that correctly classifies images of cats and dogs with at least 63% accuracy.
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在这个挑战中，你将使用 TensorFlow 2.0 和 Keras 创建一个卷积神经网络，对猫和狗的图像进行正确分类，准确率至少达到 63%。
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You can access [the full project instructions and starter code on Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-cat-and-dog-image-classifier/blob/master/fcc_cat_dog.ipynb).
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你可以访问 [Google Colaboratory 上的完整项目说明和启动代码](https://colab.research.google.com/github/freeCodeCamp/boilerplate-cat-and-dog-image-classifier/blob/master/fcc_cat_dog.ipynb)。
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After going to that link, create a copy of the notebook either in your own account or locally. Once you complete the project and it passes the test (included at that link), submit your project link below. If you are submitting a Google Colaboratory link, make sure to turn on link sharing for "anyone with the link."
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进入该链接后，在你自己的账户或本地创建一个笔记本的副本。 一旦你完成项目并通过链接中的测试，请在下面提交你的项目链接。 如果你提交的是 Google Colaboratory 的链接，请确保打开链接共享时选择 “anyone with the link”。
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We are still developing the interactive instructional content for the machine learning curriculum. For now, you can go through the video challenges in this certification. You may also have to seek out additional learning resources, similar to what you would do when working on a real-world project.
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我们仍在开发机器学习课程的交互式课程部分。 现在，你可以通过这个认证视频挑战。 你可能还需要寻找额外的学习资源，类似于你在真实世界项目中的工作。
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# --hints--
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It should pass all Python tests.
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它应该通过所有的 Python 测试。
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```js
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---
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id: 5e46f8edac417301a38fb930
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title: Linear Regression Health Costs Calculator
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title: 线性回归健康成本计算器
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challengeType: 10
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forumTopicId: 462379
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dashedName: linear-regression-health-costs-calculator
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---
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# --description--
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In this challenge, you will predict healthcare costs using a regression algorithm.
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在这个挑战中，你将使用回归算法预测医疗费用。
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You are given a dataset that contains information about different people including their healthcare costs. Use the data to predict healthcare costs based on new data.
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你会得到一个数据集，其中包含不同人的信息，包括他们的医疗费用。 用数据来预测基于新数据的医疗费用。
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You can access [the full project instructions and starter code on Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-linear-regression-health-costs-calculator/blob/master/fcc_predict_health_costs_with_regression.ipynb).
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你可以访问 [Google Colaboratory 上的完整项目说明和启动代码](https://colab.research.google.com/github/freeCodeCamp/boilerplate-linear-regression-health-costs-calculator/blob/master/fcc_predict_health_costs_with_regression.ipynb)。
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After going to that link, create a copy of the notebook either in your own account or locally. Once you complete the project and it passes the test (included at that link), submit your project link below. If you are submitting a Google Colaboratory link, make sure to turn on link sharing for "anyone with the link."
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进入该链接后，在你自己的账户或本地创建一个笔记本的副本。 一旦你完成项目并通过链接中的测试，请在下面提交你的项目链接。 如果你提交的是 Google Colaboratory 的链接，请确保打开链接共享时选择 “anyone with the link”。
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We are still developing the interactive instructional content for the machine learning curriculum. For now, you can go through the video challenges in this certification. You may also have to seek out additional learning resources, similar to what you would do when working on a real-world project.
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我们仍在开发机器学习课程的交互式课程部分。 现在，你可以通过这个认证中的视频挑战。 你可能还需要寻找额外的学习资源，类似于你在真实世界项目中的工作。
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# --hints--
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It should pass all Python tests.
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它应该通过所有的 Python 测试。
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```js
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---
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id: 5e46f8edac417301a38fb931
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title: Neural Network SMS Text Classifier
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title: 神经网络短信文本分类器
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challengeType: 10
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forumTopicId: 462380
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dashedName: neural-network-sms-text-classifier
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---
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# --description--
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In this challenge, you need to create a machine learning model that will classify SMS messages as either "ham" or "spam". A "ham" message is a normal message sent by a friend. A "spam" message is an advertisement or a message sent by a company.
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在这个挑战中，你需要创建一个机器学习模型，将短信分类为 “ham” 或 “spam”。 “ham” 信息是由一个朋友发送的普通信息。 “spam” 是一个公司发送的广告或信息。
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You can access [the full project instructions and starter code on Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-neural-network-sms-text-classifier/blob/master/fcc_sms_text_classification.ipynb).
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你可以访问 [Google Colaboratory 上的完整项目说明和启动代码](https://colab.research.google.com/github/freeCodeCamp/boilerplate-neural-network-sms-text-classifier/blob/master/fcc_sms_text_classification.ipynb)。
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After going to that link, create a copy of the notebook either in your own account or locally. Once you complete the project and it passes the test (included at that link), submit your project link below. If you are submitting a Google Colaboratory link, make sure to turn on link sharing for "anyone with the link."
|
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进入该链接后，在你自己的账户或本地创建一个笔记本的副本。 一旦你完成项目并通过链接中的测试，请在下面提交你的项目链接。 如果你提交的是 Google Colaboratory 的链接，请确保打开链接共享时选择 “anyone with the link”。
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We are still developing the interactive instructional content for the machine learning curriculum. For now, you can go through the video challenges in this certification. You may also have to seek out additional learning resources, similar to what you would do when working on a real-world project.
|
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我们仍在开发机器学习课程的交互式课程部分。 现在，你可以通过这个认证中的视频挑战。 你可能还需要寻找额外的学习资源，类似于你在真实世界项目中的工作。
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# --hints--
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It should pass all Python tests.
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它应该通过所有的 Python 测试。
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```js
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---
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id: 5e46f8d6ac417301a38fb92d
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title: Rock Paper Scissors
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title: 剪刀石头布
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challengeType: 10
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forumTopicId: 462376
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dashedName: rock-paper-scissors
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---
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# --description--
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For this challenge, you will create a program to play Rock, Paper, Scissors. A program that picks at random will usually win 50% of the time. To pass this challenge your program must play matches against four different bots, winning at least 60% of the games in each match.
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在这个挑战中，你将创建一个程序来玩石头、剪刀、布。 一个随机选取的程序通常会有 50% 的时间获胜。 要通过这一挑战，你的程序必须与四个不同的机器人进行对战，并达到至少 60% 胜率。
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You can access [the full project description and starter code on repl.it](https://repl.it/github/freeCodeCamp/boilerplate-rock-paper-scissors).
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你可以访问 [Replit 上的完整项目描述和启动代码](https://replit.com/github/freeCodeCamp/boilerplate-rock-paper-scissors)。
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After going to that link, fork the project. Once you complete the project based on the instructions in 'README.md', submit your project link below.
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进入该链接后，fork 该项目。 一旦你根据 “README.md” 中的说明完成了项目，请在下面提交你的项目链接。
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We are still developing the interactive instructional part of the machine learning curriculum. For now, you will have to use other resources to learn how to pass this challenge.
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我们仍在开发机器学习课程的交互式课程部分。 现在，你需要使用其他资源来学习如何通过这一挑战。
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# --hints--
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It should pass all Python tests.
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它应该通过所有的 Python 测试。
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```js
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---
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id: 5e8f2f13c4cdbe86b5c72da6
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title: Conclusion
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title: 结论
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challengeType: 11
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videoId: LMNub5frQi4
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dashedName: conclusion
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## --text--
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Most people that are experts in AI or machine learning usually...:
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大多数人工智能或机器学习专家通常......
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## --answers--
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have one specialization.
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有一个专业。
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---
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have many specializations.
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有很多专业。
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---
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have a deep understanding of many different frameworks.
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对许多不同的框架有深入的了解。
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## --video-solution--
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---
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id: 5e8f2f13c4cdbe86b5c72d99
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title: 'Convolutional Neural Networks: Evaluating the Model'
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title: '卷积神经网络：评估模型'
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challengeType: 11
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videoId: eCATNvwraXg
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dashedName: convolutional-neural-networks-evaluating-the-model
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## --text--
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What is **not** a good way to increase the accuracy of a convolutional neural network?
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什么 **不是** 提高卷积神经网络准确性的好方法？
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## --answers--
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Augmenting the data you already have.
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扩充你已有的数据。
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---
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Using a pre-trained model.
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使用预先训练的模型。
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---
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Using your test data to retrain the model.
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使用你的测试数据重新训练模型。
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## --video-solution--
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@ -1,6 +1,6 @@
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---
|
||||
id: 5e8f2f13c4cdbe86b5c72d9a
|
||||
title: 'Convolutional Neural Networks: Picking a Pretrained Model'
|
||||
title: '卷积神经网络：选择预训练模型'
|
||||
challengeType: 11
|
||||
videoId: h1XUt1AgIOI
|
||||
dashedName: convolutional-neural-networks-picking-a-pretrained-model
|
||||
@ -10,7 +10,7 @@ dashedName: convolutional-neural-networks-picking-a-pretrained-model
|
||||
|
||||
## --text--
|
||||
|
||||
Fill in the blanks below to use Google's pre-trained MobileNet V2 model as a base for a convolutional neural network:
|
||||
填写下面的空白，使用谷歌预训练的 MobileNet V2 模型作为卷积神经网络的基础：
|
||||
|
||||
```py
|
||||
base_model = tf.__A__.applications.__B__(input_shape=(160, 160, 3),
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d97
|
||||
title: 'Convolutional Neural Networks: The Convolutional Layer'
|
||||
title: '卷积神经网络：卷积层'
|
||||
challengeType: 11
|
||||
videoId: LrdmcQpTyLw
|
||||
dashedName: convolutional-neural-networks-the-convolutional-layer
|
||||
@ -10,19 +10,19 @@ dashedName: convolutional-neural-networks-the-convolutional-layer
|
||||
|
||||
## --text--
|
||||
|
||||
What are the three main properties of each convolutional layer?
|
||||
每个卷积层的三个主要属性是什么？
|
||||
|
||||
## --answers--
|
||||
|
||||
Input size, the number of filters, and the sample size of the filters.
|
||||
输入大小、过滤器数量和过滤器的样本大小。
|
||||
|
||||
---
|
||||
|
||||
Input size, input dimensions, and the color values of the input.
|
||||
输入大小、输入尺寸和输入的颜色值。
|
||||
|
||||
---
|
||||
|
||||
Input size, input padding, and stride.
|
||||
输入大小、输入填充和步长。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d96
|
||||
title: Convolutional Neural Networks
|
||||
title: 卷积神经网络
|
||||
challengeType: 11
|
||||
videoId: _1kTP7uoU9E
|
||||
dashedName: convolutional-neural-networks
|
||||
@ -10,19 +10,19 @@ dashedName: convolutional-neural-networks
|
||||
|
||||
## --text--
|
||||
|
||||
Dense neural networks analyze input on a global scale and recognize patterns in specific areas. Convolutional neural networks...:
|
||||
密集神经网络在全局范围内分析输入，并识别特定区域的模式。 卷积神经网络......：
|
||||
|
||||
## --answers--
|
||||
|
||||
also analyze input globally and extract features from specific areas.
|
||||
也在全局分析输入并从特定区域提取特征。
|
||||
|
||||
---
|
||||
|
||||
do not work well for image classification or object detection.
|
||||
在图像分类或物体检测方面效果不佳。
|
||||
|
||||
---
|
||||
|
||||
scan through the entire input a little at a time and learn local patterns.
|
||||
每次一点点地扫描整个输入，并学习局部模式。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d8e
|
||||
title: 'Core Learning Algorithms: Building the Model'
|
||||
title: '核心学习算法：构建模型'
|
||||
challengeType: 11
|
||||
videoId: 5wHw8BTd2ZQ
|
||||
dashedName: core-learning-algorithms-building-the-model
|
||||
@ -10,7 +10,7 @@ dashedName: core-learning-algorithms-building-the-model
|
||||
|
||||
## --text--
|
||||
|
||||
What kind of estimator/model does TensorFlow recommend using for classification?
|
||||
TensorFlow 推荐使用哪种估计器/模型进行分类？
|
||||
|
||||
## --answers--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d8d
|
||||
title: 'Core Learning Algorithms: Classification'
|
||||
title: '核心学习算法：分类'
|
||||
challengeType: 11
|
||||
videoId: qFF7ZQNvK9E
|
||||
dashedName: core-learning-algorithms-classification
|
||||
@ -10,19 +10,19 @@ dashedName: core-learning-algorithms-classification
|
||||
|
||||
## --text--
|
||||
|
||||
What is classification?
|
||||
什么是分类？
|
||||
|
||||
## --answers--
|
||||
|
||||
The process of separating data points into different classes.
|
||||
将数据点分离成不同类别的过程。
|
||||
|
||||
---
|
||||
|
||||
Predicting a numeric value or forecast based on independent and dependent variables.
|
||||
根据自变量和因变量预测数值或预测。
|
||||
|
||||
---
|
||||
|
||||
None of the above.
|
||||
以上都不是。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d8f
|
||||
title: 'Core Learning Algorithms: Clustering'
|
||||
title: '核心学习算法：聚类'
|
||||
challengeType: 11
|
||||
videoId: 8sqIaHc9Cz4
|
||||
dashedName: core-learning-algorithms-clustering
|
||||
@ -10,27 +10,27 @@ dashedName: core-learning-algorithms-clustering
|
||||
|
||||
## --text--
|
||||
|
||||
Which of the following steps is **not** part of the K-Means algorithm?
|
||||
以下哪个步骤 **不是** K-Means 算法的一部分？
|
||||
|
||||
## --answers--
|
||||
|
||||
Randomly pick K points to place K centeroids.
|
||||
随机选取 K 个点放置 K 个质心。
|
||||
|
||||
---
|
||||
|
||||
Assign each K point to the closest K centeroid.
|
||||
将每个 K 点分配给最近的 K 质心。
|
||||
|
||||
---
|
||||
|
||||
Move each K centeroid into the middle of all of their data points.
|
||||
将每个 K 质心移动到其所有数据点的中间。
|
||||
|
||||
---
|
||||
|
||||
Shuffle the K points so they're redistributed randomly.
|
||||
打乱 K 点，使它们随机重新分配。
|
||||
|
||||
---
|
||||
|
||||
Reassign each K point to the closest K centeroid.
|
||||
重新分配每个 K 点给最近的 K 质心。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d90
|
||||
title: 'Core Learning Algorithms: Hidden Markov Models'
|
||||
title: '核心学习算法：隐马尔可夫模型'
|
||||
challengeType: 11
|
||||
videoId: IZg24y4wEPY
|
||||
dashedName: core-learning-algorithms-hidden-markov-models
|
||||
@ -10,19 +10,19 @@ dashedName: core-learning-algorithms-hidden-markov-models
|
||||
|
||||
## --text--
|
||||
|
||||
What makes a Hidden Markov model different than linear regression or classification?
|
||||
隐马尔科夫模型与线性回归或分类有何不同？
|
||||
|
||||
## --answers--
|
||||
|
||||
It uses probability distributions to predict future events or states.
|
||||
它使用概率分布来预测未来的事件或状态。
|
||||
|
||||
---
|
||||
|
||||
It analyzes the relationship between independent and dependent variables to make predictions.
|
||||
它分析自变量和因变量之间的关系以进行预测。
|
||||
|
||||
---
|
||||
|
||||
It separates data points into separate categories.
|
||||
它将数据点分成不同的类别。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d8c
|
||||
title: 'Core Learning Algorithms: The Training Process'
|
||||
title: '核心学习算法：训练过程'
|
||||
challengeType: 11
|
||||
videoId: _cEwvqVoBhI
|
||||
dashedName: core-learning-algorithms-the-training-process
|
||||
@ -10,19 +10,19 @@ dashedName: core-learning-algorithms-the-training-process
|
||||
|
||||
## --text--
|
||||
|
||||
What are epochs?
|
||||
什么是 epoch？
|
||||
|
||||
## --answers--
|
||||
|
||||
The number of times the model will see the same data.
|
||||
模型看到相同数据的次数。
|
||||
|
||||
---
|
||||
|
||||
A type of graph.
|
||||
一种图。
|
||||
|
||||
---
|
||||
|
||||
The number of elements you feed to the model at once.
|
||||
你一次提供给模型的元素数量。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d8b
|
||||
title: 'Core Learning Algorithms: Training and Testing Data'
|
||||
title: '核心学习算法：训练和测试数据'
|
||||
challengeType: 11
|
||||
videoId: wz9J1slsi7I
|
||||
dashedName: core-learning-algorithms-training-and-testing-data
|
||||
@ -10,19 +10,19 @@ dashedName: core-learning-algorithms-training-and-testing-data
|
||||
|
||||
## --text--
|
||||
|
||||
What is categorical data?
|
||||
什么是分类数据？
|
||||
|
||||
## --answers--
|
||||
|
||||
Another term for one-hot encoding.
|
||||
独热编码的另一个术语。
|
||||
|
||||
---
|
||||
|
||||
Any data that is not numeric.
|
||||
任何非数字的数据。
|
||||
|
||||
---
|
||||
|
||||
Any data that is represented numerically.
|
||||
任何以数字表示的数据。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d91
|
||||
title: 'Core Learning Algorithms: Using Probabilities to make Predictions'
|
||||
title: '核心学习算法：使用概率进行预测'
|
||||
challengeType: 11
|
||||
videoId: fYAYvLUawnc
|
||||
dashedName: core-learning-algorithms-using-probabilities-to-make-predictions
|
||||
@ -10,7 +10,7 @@ dashedName: core-learning-algorithms-using-probabilities-to-make-predictions
|
||||
|
||||
## --text--
|
||||
|
||||
What TensorFlow module should you import to implement `.HiddenMarkovModel()`?
|
||||
你应该导入什么 TensorFlow 模块来实现 `.HiddenMarkovModel()`？
|
||||
|
||||
## --answers--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d8a
|
||||
title: 'Core Learning Algorithms: Working with Data'
|
||||
title: '核心学习算法：处理数据'
|
||||
challengeType: 11
|
||||
videoId: u85IOSsJsPI
|
||||
dashedName: core-learning-algorithms-working-with-data
|
||||
@ -10,19 +10,19 @@ dashedName: core-learning-algorithms-working-with-data
|
||||
|
||||
## --text--
|
||||
|
||||
What does the pandas `.head()` function do?
|
||||
Pandas 的 `.head()` 函数有什么作用？
|
||||
|
||||
## --answers--
|
||||
|
||||
Returns the number of entries in a data frame.
|
||||
返回数据框中的条目数。
|
||||
|
||||
---
|
||||
|
||||
Returns the number of columns in a data frame.
|
||||
返回数据框中的列数。
|
||||
|
||||
---
|
||||
|
||||
By default, shows the first five rows or entries in a data frame.
|
||||
默认情况下，显示数据框中的前五行或条目。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d89
|
||||
title: Core Learning Algorithms
|
||||
title: 核心学习算法
|
||||
challengeType: 11
|
||||
videoId: u5lZURgcWnU
|
||||
dashedName: core-learning-algorithms
|
||||
@ -10,25 +10,25 @@ dashedName: core-learning-algorithms
|
||||
|
||||
## --text--
|
||||
|
||||
Which type of analysis would be best suited for the following problem?:
|
||||
哪种类型的分析最适合以下问题？
|
||||
|
||||
You have the average temperature in the month of March for the last 100 years. Using this data, you want to predict the average temperature in the month of March 5 years from now.
|
||||
你拥有过去 100 年三月份的平均温度。 使用此数据，你希望预测 5 年后 3 月的平均温度。
|
||||
|
||||
## --answers--
|
||||
|
||||
Multiple regression
|
||||
多重回归
|
||||
|
||||
---
|
||||
|
||||
Correlation
|
||||
关连
|
||||
|
||||
---
|
||||
|
||||
Decision tree
|
||||
决策树
|
||||
|
||||
---
|
||||
|
||||
Linear regression
|
||||
线性回归
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d98
|
||||
title: Creating a Convolutional Neural Network
|
||||
title: 创建卷积神经网络
|
||||
challengeType: 11
|
||||
videoId: kfv0K8MtkIc
|
||||
dashedName: creating-a-convolutional-neural-network
|
||||
@ -10,7 +10,7 @@ dashedName: creating-a-convolutional-neural-network
|
||||
|
||||
## --text--
|
||||
|
||||
Fill in the blanks below to complete the architecture for a convolutional neural network:
|
||||
填写下面的空白以完成卷积神经网络的架构：
|
||||
|
||||
```py
|
||||
model = models.__A__()
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d87
|
||||
title: 'Introduction: Machine Learning Fundamentals'
|
||||
title: '简介：机器学习基础'
|
||||
challengeType: 11
|
||||
videoId: KwL1qTR5MT8
|
||||
dashedName: introduction-machine-learning-fundamentals
|
||||
@ -10,19 +10,19 @@ dashedName: introduction-machine-learning-fundamentals
|
||||
|
||||
## --text--
|
||||
|
||||
Which statement below is **false**?
|
||||
以下哪个陈述是 **假的**？
|
||||
|
||||
## --answers--
|
||||
|
||||
Neural networks are modeled after the way the human brain works.
|
||||
神经网络以人脑的工作方式为模型。
|
||||
|
||||
---
|
||||
|
||||
Computer programs that play tic-tac-toe or chess against human players are examples of simple artificial intelligence.
|
||||
与人类玩家玩井字棋或国际象棋的计算机程序是简单人工智能的例子。
|
||||
|
||||
---
|
||||
|
||||
Machine learning is a subset of artificial intelligence.
|
||||
机器学习是人工智能的一个子集。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d88
|
||||
title: Introduction to TensorFlow
|
||||
title: TensorFlow 简介
|
||||
challengeType: 11
|
||||
videoId: r9hRyGGjOgQ
|
||||
dashedName: introduction-to-tensorflow
|
||||
@ -10,11 +10,11 @@ dashedName: introduction-to-tensorflow
|
||||
|
||||
## --text--
|
||||
|
||||
Which of the following is **not** a type of tensor?
|
||||
以下哪个 **不是** 张量的类型？
|
||||
|
||||
## --answers--
|
||||
|
||||
Variable
|
||||
变量
|
||||
|
||||
---
|
||||
|
||||
@ -22,15 +22,15 @@ Flowing
|
||||
|
||||
---
|
||||
|
||||
Placeholder
|
||||
占位符
|
||||
|
||||
---
|
||||
|
||||
SparseTensor
|
||||
稀疏张量
|
||||
|
||||
---
|
||||
|
||||
Constant
|
||||
常量
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72da1
|
||||
title: 'Natural Language Processing With RNNs: Building the Model'
|
||||
title: '使用 RNN 处理自然语言：构建模型'
|
||||
challengeType: 11
|
||||
videoId: 32WBFS7lfsw
|
||||
dashedName: natural-language-processing-with-rnns-building-the-model
|
||||
@ -10,7 +10,7 @@ dashedName: natural-language-processing-with-rnns-building-the-model
|
||||
|
||||
## --text--
|
||||
|
||||
Fill in the blanks below to complete the `build_model` function:
|
||||
填写下面的空白以完成 `build_model` 函数：
|
||||
|
||||
```py
|
||||
def build_mode(vocab_size, embedding_dim, rnn_units, batch_size):
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72da0
|
||||
title: 'Natural Language Processing With RNNs: Create a Play Generator'
|
||||
title: '使用 RNN 进行自然语言处理：创建戏剧生成器'
|
||||
challengeType: 11
|
||||
videoId: j5xsxjq_Xk8
|
||||
dashedName: natural-language-processing-with-rnns-create-a-play-generator
|
||||
@ -10,7 +10,7 @@ dashedName: natural-language-processing-with-rnns-create-a-play-generator
|
||||
|
||||
## --text--
|
||||
|
||||
Fill in the blanks below to create the training examples for the RNN:
|
||||
填写下面的空白以创建 RNN 的训练示例：
|
||||
|
||||
```py
|
||||
char_dataset = tf.data.__A__.__B__(text_as_int)
|
||||
@ -18,7 +18,7 @@ char_dataset = tf.data.__A__.__B__(text_as_int)
|
||||
|
||||
## --answers--
|
||||
|
||||
A: `DataSet`
|
||||
A: `Dataset`
|
||||
|
||||
B: `from_tensor_slices`
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d9f
|
||||
title: 'Natural Language Processing With RNNs: Making Predictions'
|
||||
title: '使用 RNN 进行自然语言处理：进行预测'
|
||||
challengeType: 11
|
||||
videoId: WO1hINnBj20
|
||||
dashedName: natural-language-processing-with-rnns-making-predictions
|
||||
@ -10,19 +10,19 @@ dashedName: natural-language-processing-with-rnns-making-predictions
|
||||
|
||||
## --text--
|
||||
|
||||
Before you make a prediction with your own review, you should...:
|
||||
在你通过自己的评论做出预测之前，你应该：
|
||||
|
||||
## --answers--
|
||||
|
||||
decode the training dataset and compare the results to the test data.
|
||||
对训练数据集进行解码，并将结果与测试数据进行比较。
|
||||
|
||||
---
|
||||
|
||||
use the encodings from the training dataset to encode your review.
|
||||
使用训练数据集的编码对你的评论进行编码。
|
||||
|
||||
---
|
||||
|
||||
assign random values between 0 and the maximum number of vocabulary in your dataset to each word in your review.
|
||||
将 0 和数据集中最大词汇量之间的值，随机分配给你的评论中的每个单词。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d9c
|
||||
title: 'Natural Language Processing With RNNs: Part 2'
|
||||
title: '使用 RNN 进行自然语言处理：第 2 部分'
|
||||
challengeType: 11
|
||||
videoId: mUU9YXOFbZg
|
||||
dashedName: natural-language-processing-with-rnns-part-2
|
||||
@ -10,19 +10,19 @@ dashedName: natural-language-processing-with-rnns-part-2
|
||||
|
||||
## --text--
|
||||
|
||||
Word embeddings are...:
|
||||
词嵌入是指...：
|
||||
|
||||
## --answers--
|
||||
|
||||
an unordered group of encoded words that describes the frequency of words in a given document.
|
||||
一组无序的编码词，描述给定文档中词的频率。
|
||||
|
||||
---
|
||||
|
||||
a group of encoded words that preserves the original order of the words in a given document.
|
||||
一组编码单词，保留给定文档中单词的原始顺序。
|
||||
|
||||
---
|
||||
|
||||
a vectorized representation of words in a given document that places words with similar meanings near each other.
|
||||
给定文档中单词的矢量化表示，将具有相似含义的单词彼此靠近。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d9d
|
||||
title: 'Natural Language Processing With RNNs: Recurring Neural Networks'
|
||||
title: '使用 RNN 进行自然语言处理：循环神经网络'
|
||||
challengeType: 11
|
||||
videoId: bX5681NPOcA
|
||||
dashedName: natural-language-processing-with-rnns-recurring-neural-networks
|
||||
@ -10,23 +10,23 @@ dashedName: natural-language-processing-with-rnns-recurring-neural-networks
|
||||
|
||||
## --text--
|
||||
|
||||
What is true about Recurrent Neural Networks?
|
||||
关于循环神经网络，哪一项是正确的？
|
||||
|
||||
## --answers--
|
||||
|
||||
1: They are a type of feed-forward neural network.
|
||||
1：它们是一种前馈神经网络。
|
||||
|
||||
---
|
||||
|
||||
2: They maintain an internal memory/state of the input that was already processed.
|
||||
2：它们保持着一个已经处理过的输入的内部存储器/状态。
|
||||
|
||||
---
|
||||
|
||||
3: RNN's contain a loop and process one piece of input at a time.
|
||||
3：RNN 包含一个循环，每次处理一个输入。
|
||||
|
||||
---
|
||||
|
||||
4: Both 2 and 3.
|
||||
4：2 和 3。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d9e
|
||||
title: 'Natural Language Processing With RNNs: Sentiment Analysis'
|
||||
title: '使用 RNN 进行自然语言处理：情感分析'
|
||||
challengeType: 11
|
||||
videoId: lYeLtu8Nq7c
|
||||
dashedName: natural-language-processing-with-rnns-sentiment-analysis
|
||||
@ -10,7 +10,7 @@ dashedName: natural-language-processing-with-rnns-sentiment-analysis
|
||||
|
||||
## --text--
|
||||
|
||||
Fill in the blanks below to create the model for the RNN:
|
||||
填写下面的空白来创建 RNN 模型：
|
||||
|
||||
```py
|
||||
model = __A__.keras.Sequential([
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72da2
|
||||
title: 'Natural Language Processing With RNNs: Training the Model'
|
||||
title: '使用 RNN 进行自然语言处理：训练模型'
|
||||
challengeType: 11
|
||||
videoId: hEUiK7j9UI8
|
||||
dashedName: natural-language-processing-with-rnns-training-the-model
|
||||
@ -10,7 +10,7 @@ dashedName: natural-language-processing-with-rnns-training-the-model
|
||||
|
||||
## --text--
|
||||
|
||||
Fill in the blanks below to save your model's checkpoints in the `./checkpoints` directory and call the latest checkpoint for training:
|
||||
填写下面的空白以将你模型的检查点保存在 `./checkpoints` 目录中，并调用最新的检查点进行训练：
|
||||
|
||||
```py
|
||||
checkpoint_dir = __A__
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d9b
|
||||
title: Natural Language Processing With RNNs
|
||||
title: 使用 RNN 的自然语言处理
|
||||
challengeType: 11
|
||||
videoId: ZyCaF5S-lKg
|
||||
dashedName: natural-language-processing-with-rnns
|
||||
@ -10,19 +10,19 @@ dashedName: natural-language-processing-with-rnns
|
||||
|
||||
## --text--
|
||||
|
||||
Natural Language Processing is a branch of artificial intelligence that...:
|
||||
自然语言处理是人工智能的一个分支......：
|
||||
|
||||
## --answers--
|
||||
|
||||
deals with how computers understand and process natural/human languages.
|
||||
解决计算机如何理解和处理自然语言/人文语言。
|
||||
|
||||
---
|
||||
|
||||
translates image data into natural/human languages.
|
||||
将图像数据转换成自然/人文语言。
|
||||
|
||||
---
|
||||
|
||||
is focused on translating computer languages into natural/human languages.
|
||||
重点是将计算机语言翻译成自然/人文语言。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d93
|
||||
title: 'Neural Networks: Activation Functions'
|
||||
title: '神经网络：激活函数'
|
||||
challengeType: 11
|
||||
videoId: S45tqW6BqRs
|
||||
dashedName: neural-networks-activation-functions
|
||||
@ -10,15 +10,15 @@ dashedName: neural-networks-activation-functions
|
||||
|
||||
## --text--
|
||||
|
||||
Which activation function switches values between -1 and 1?
|
||||
哪个激活函数在 -1 和 1 之间切换值？
|
||||
|
||||
## --answers--
|
||||
|
||||
ReLU (Rectified Linear Unit)
|
||||
ReLU（线性整流函数）
|
||||
|
||||
---
|
||||
|
||||
Tanh (Hyperbolic Tangent)
|
||||
Tanh（双曲函数）
|
||||
|
||||
---
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d95
|
||||
title: 'Neural Networks: Creating a Model'
|
||||
title: '神经网络：创建模型'
|
||||
challengeType: 11
|
||||
videoId: K8bz1bmOCTw
|
||||
dashedName: neural-networks-creating-a-model
|
||||
@ -10,7 +10,7 @@ dashedName: neural-networks-creating-a-model
|
||||
|
||||
## --text--
|
||||
|
||||
Fill in the blanks below to build a sequential model of dense layers:
|
||||
填写下面的空白，建立一个密集层的顺序模型。
|
||||
|
||||
```py
|
||||
model = __A__.__B__([
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d94
|
||||
title: 'Neural Networks: Optimizers'
|
||||
title: '神经网络：优化'
|
||||
challengeType: 11
|
||||
videoId: hdOtRPQe1o4
|
||||
dashedName: neural-networks-optimizers
|
||||
@ -10,19 +10,19 @@ dashedName: neural-networks-optimizers
|
||||
|
||||
## --text--
|
||||
|
||||
What is an optimizer function?
|
||||
什么是优化函数？
|
||||
|
||||
## --answers--
|
||||
|
||||
A function that increases the accuracy of a model's predictions.
|
||||
一种提高模型预测准确性的函数。
|
||||
|
||||
---
|
||||
|
||||
A function that implements the gradient descent and backpropagation algorithms for you.
|
||||
一个为你实现梯度下降和反向传播算法的函数。
|
||||
|
||||
---
|
||||
|
||||
A function that reduces the time a model needs to train.
|
||||
一个可以减少模型训练时间的函数。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d92
|
||||
title: Neural Networks with TensorFlow
|
||||
title: 使用 TensorFlow 的神经网络
|
||||
challengeType: 11
|
||||
videoId: uisdfrNrZW4
|
||||
dashedName: neural-networks-with-tensorflow
|
||||
@ -10,19 +10,19 @@ dashedName: neural-networks-with-tensorflow
|
||||
|
||||
## --text--
|
||||
|
||||
A densely connected neural network is one in which...:
|
||||
一个密集连接的神经网络是这样一种网络：
|
||||
|
||||
## --answers--
|
||||
|
||||
all the neurons in the current layer are connected to one neuron in the previous layer.
|
||||
当前层中的所有神经元都连接到前一层中的一个神经元。
|
||||
|
||||
---
|
||||
|
||||
all the neurons in each layer are connected randomly.
|
||||
每层的所有神经元都是随机连接的。
|
||||
|
||||
---
|
||||
|
||||
all the neurons in the current layer are connected to every neuron in the previous layer.
|
||||
当前层中的所有神经元都连接到前一层中的每个神经元。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72da5
|
||||
title: 'Reinforcement Learning With Q-Learning: Example'
|
||||
title: '使用 Q-Learning 进行强化学习：示例'
|
||||
challengeType: 11
|
||||
videoId: RBBSNta234s
|
||||
dashedName: reinforcement-learning-with-q-learning-example
|
||||
@ -10,7 +10,7 @@ dashedName: reinforcement-learning-with-q-learning-example
|
||||
|
||||
## --text--
|
||||
|
||||
Fill in the blanks to complete the following Q-Learning equation:
|
||||
填空以完成以下 Q-Learning 方程：
|
||||
|
||||
```py
|
||||
Q[__A__, __B__] = Q[__A__, __B__] + LEARNING_RATE * (reward + GAMMA * np.max(Q[__C__, :]) - Q[__A__, __B__])
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72da4
|
||||
title: 'Reinforcement Learning With Q-Learning: Part 2'
|
||||
title: '使用 Q-Learning 进行强化学习：第 2 部分'
|
||||
challengeType: 11
|
||||
videoId: DX7hJuaUZ7o
|
||||
dashedName: reinforcement-learning-with-q-learning-part-2
|
||||
@ -10,15 +10,15 @@ dashedName: reinforcement-learning-with-q-learning-part-2
|
||||
|
||||
## --text--
|
||||
|
||||
What can happen if the agent does not have a good balance of taking random actions and using learned actions?
|
||||
如果智能体在采取随机动作和使用学习动作之间没有很好的平衡，会发生什么？
|
||||
|
||||
## --answers--
|
||||
|
||||
The agent will always try to minimize its reward for the current state/action, leading to local minima.
|
||||
智能体将始终尝试将其对当前状态/动作的奖励最小化，从而导致局部最小值。
|
||||
|
||||
---
|
||||
|
||||
The agent will always try to maximize its reward for the current state/action, leading to local maxima.
|
||||
智能体将始终尝试将其对当前状态/动作的奖励最大化，从而导致局部最大值。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72da3
|
||||
title: Reinforcement Learning With Q-Learning
|
||||
title: 使用 Q-Learning 进行强化学习
|
||||
challengeType: 11
|
||||
videoId: Cf7DSU0gVb4
|
||||
dashedName: reinforcement-learning-with-q-learning
|
||||
@ -10,19 +10,19 @@ dashedName: reinforcement-learning-with-q-learning
|
||||
|
||||
## --text--
|
||||
|
||||
The key components of reinforcement learning are...
|
||||
强化学习的关键组成部分是......
|
||||
|
||||
## --answers--
|
||||
|
||||
environment, representative, state, reaction, and reward.
|
||||
环境、代表、状态、反应和奖励。
|
||||
|
||||
---
|
||||
|
||||
environment, agent, state, action, and reward.
|
||||
环境、代理、状态、动作和奖励。
|
||||
|
||||
---
|
||||
|
||||
habitat, agent, state, action, and punishment.
|
||||
环境、代理、状态、动作和惩罚。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
Reference in New Issue
Block a user