chore(i18n,chn): manually downloaded curriculum (#42858)
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Mrugesh Mohapatra
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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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@ -10,19 +10,19 @@ 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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---
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id: 5e8f2f13c4cdbe86b5c72d9a
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title: 'Convolutional Neural Networks: Picking a Pretrained Model'
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title: '卷积神经网络：选择预训练模型'
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challengeType: 11
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videoId: h1XUt1AgIOI
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dashedName: convolutional-neural-networks-picking-a-pretrained-model
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## --text--
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Fill in the blanks below to use Google's pre-trained MobileNet V2 model as a base for a convolutional neural network:
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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：
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```py
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base_model = tf.__A__.applications.__B__(input_shape=(160, 160, 3),
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---
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id: 5e8f2f13c4cdbe86b5c72d97
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title: 'Convolutional Neural Networks: The Convolutional Layer'
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title: '卷积神经网络：卷积层'
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challengeType: 11
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videoId: LrdmcQpTyLw
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dashedName: convolutional-neural-networks-the-convolutional-layer
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## --text--
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What are the three main properties of each convolutional layer?
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每个卷积层的三个主要属性是什么？
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## --answers--
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Input size, the number of filters, and the sample size of the filters.
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输入大小、过滤器数量和过滤器的样本大小。
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---
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Input size, input dimensions, and the color values of the input.
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输入大小、输入尺寸和输入的颜色值。
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---
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Input size, input padding, and stride.
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输入大小、输入填充和步长。
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## --video-solution--
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---
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id: 5e8f2f13c4cdbe86b5c72d96
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title: Convolutional Neural Networks
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title: 卷积神经网络
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challengeType: 11
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videoId: _1kTP7uoU9E
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dashedName: convolutional-neural-networks
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## --text--
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Dense neural networks analyze input on a global scale and recognize patterns in specific areas. Convolutional neural networks...:
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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......：
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## --answers--
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also analyze input globally and extract features from specific areas.
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也在全局分析输入并从特定区域提取特征。
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---
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do not work well for image classification or object detection.
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在图像分类或物体检测方面效果不佳。
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---
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scan through the entire input a little at a time and learn local patterns.
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每次一点点地扫描整个输入，并学习局部模式。
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## --video-solution--
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---
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id: 5e8f2f13c4cdbe86b5c72d8e
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title: 'Core Learning Algorithms: Building the Model'
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title: '核心学习算法：构建模型'
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challengeType: 11
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videoId: 5wHw8BTd2ZQ
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dashedName: core-learning-algorithms-building-the-model
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## --text--
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What kind of estimator/model does TensorFlow recommend using for classification?
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TensorFlow 推荐使用哪种估计器/模型进行分类？
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## --answers--
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---
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id: 5e8f2f13c4cdbe86b5c72d8d
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title: 'Core Learning Algorithms: Classification'
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title: '核心学习算法：分类'
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challengeType: 11
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videoId: qFF7ZQNvK9E
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dashedName: core-learning-algorithms-classification
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## --text--
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What is classification?
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什么是分类？
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## --answers--
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The process of separating data points into different classes.
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将数据点分离成不同类别的过程。
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---
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Predicting a numeric value or forecast based on independent and dependent variables.
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根据自变量和因变量预测数值或预测。
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---
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None of the above.
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以上都不是。
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## --video-solution--
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---
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id: 5e8f2f13c4cdbe86b5c72d8f
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title: 'Core Learning Algorithms: Clustering'
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title: '核心学习算法：聚类'
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challengeType: 11
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videoId: 8sqIaHc9Cz4
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dashedName: core-learning-algorithms-clustering
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## --text--
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Which of the following steps is **not** part of the K-Means algorithm?
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以下哪个步骤 **不是** K-Means 算法的一部分？
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## --answers--
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Randomly pick K points to place K centeroids.
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随机选取 K 个点放置 K 个质心。
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---
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Assign each K point to the closest K centeroid.
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将每个 K 点分配给最近的 K 质心。
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---
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Move each K centeroid into the middle of all of their data points.
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将每个 K 质心移动到其所有数据点的中间。
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---
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Shuffle the K points so they're redistributed randomly.
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打乱 K 点，使它们随机重新分配。
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---
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Reassign each K point to the closest K centeroid.
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重新分配每个 K 点给最近的 K 质心。
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## --video-solution--
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---
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id: 5e8f2f13c4cdbe86b5c72d90
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title: 'Core Learning Algorithms: Hidden Markov Models'
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title: '核心学习算法：隐马尔可夫模型'
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challengeType: 11
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videoId: IZg24y4wEPY
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dashedName: core-learning-algorithms-hidden-markov-models
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## --text--
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What makes a Hidden Markov model different than linear regression or classification?
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隐马尔科夫模型与线性回归或分类有何不同？
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## --answers--
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It uses probability distributions to predict future events or states.
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它使用概率分布来预测未来的事件或状态。
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---
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It analyzes the relationship between independent and dependent variables to make predictions.
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它分析自变量和因变量之间的关系以进行预测。
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---
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It separates data points into separate categories.
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它将数据点分成不同的类别。
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## --video-solution--
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---
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id: 5e8f2f13c4cdbe86b5c72d8c
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title: 'Core Learning Algorithms: The Training Process'
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title: '核心学习算法：训练过程'
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challengeType: 11
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videoId: _cEwvqVoBhI
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dashedName: core-learning-algorithms-the-training-process
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## --text--
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What are epochs?
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什么是 epoch？
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## --answers--
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The number of times the model will see the same data.
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模型看到相同数据的次数。
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---
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A type of graph.
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一种图。
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---
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The number of elements you feed to the model at once.
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你一次提供给模型的元素数量。
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## --video-solution--
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---
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id: 5e8f2f13c4cdbe86b5c72d8b
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title: 'Core Learning Algorithms: Training and Testing Data'
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title: '核心学习算法：训练和测试数据'
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challengeType: 11
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videoId: wz9J1slsi7I
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dashedName: core-learning-algorithms-training-and-testing-data
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## --text--
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What is categorical data?
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什么是分类数据？
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## --answers--
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Another term for one-hot encoding.
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独热编码的另一个术语。
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---
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Any data that is not numeric.
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任何非数字的数据。
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---
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Any data that is represented numerically.
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任何以数字表示的数据。
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## --video-solution--
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---
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id: 5e8f2f13c4cdbe86b5c72d91
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title: 'Core Learning Algorithms: Using Probabilities to make Predictions'
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title: '核心学习算法：使用概率进行预测'
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challengeType: 11
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videoId: fYAYvLUawnc
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dashedName: core-learning-algorithms-using-probabilities-to-make-predictions
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## --text--
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What TensorFlow module should you import to implement `.HiddenMarkovModel()`?
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你应该导入什么 TensorFlow 模块来实现 `.HiddenMarkovModel()`？
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## --answers--
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---
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id: 5e8f2f13c4cdbe86b5c72d8a
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title: 'Core Learning Algorithms: Working with Data'
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title: '核心学习算法：处理数据'
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challengeType: 11
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videoId: u85IOSsJsPI
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dashedName: core-learning-algorithms-working-with-data
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## --text--
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What does the pandas `.head()` function do?
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Pandas 的 `.head()` 函数有什么作用？
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## --answers--
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Returns the number of entries in a data frame.
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返回数据框中的条目数。
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---
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Returns the number of columns in a data frame.
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返回数据框中的列数。
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---
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By default, shows the first five rows or entries in a data frame.
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默认情况下，显示数据框中的前五行或条目。
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## --video-solution--
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---
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id: 5e8f2f13c4cdbe86b5c72d89
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title: Core Learning Algorithms
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title: 核心学习算法
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challengeType: 11
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videoId: u5lZURgcWnU
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dashedName: core-learning-algorithms
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## --text--
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Which type of analysis would be best suited for the following problem?:
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哪种类型的分析最适合以下问题？
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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.
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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。
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## --answers--
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Multiple regression
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多重回归
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---
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Correlation
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关连
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---
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Decision tree
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决策树
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---
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Linear regression
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线性回归
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## --video-solution--
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---
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id: 5e8f2f13c4cdbe86b5c72d98
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title: Creating a Convolutional Neural Network
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title: 创建卷积神经网络
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challengeType: 11
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videoId: kfv0K8MtkIc
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dashedName: creating-a-convolutional-neural-network
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## --text--
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Fill in the blanks below to complete the architecture for a convolutional neural network:
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填写下面的空白以完成卷积神经网络的架构：
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```py
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model = models.__A__()
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---
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id: 5e8f2f13c4cdbe86b5c72d87
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title: 'Introduction: Machine Learning Fundamentals'
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title: '简介：机器学习基础'
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challengeType: 11
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videoId: KwL1qTR5MT8
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dashedName: introduction-machine-learning-fundamentals
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## --text--
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Which statement below is **false**?
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以下哪个陈述是 **假的**？
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## --answers--
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Neural networks are modeled after the way the human brain works.
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神经网络以人脑的工作方式为模型。
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---
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Computer programs that play tic-tac-toe or chess against human players are examples of simple artificial intelligence.
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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。
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---
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Machine learning is a subset of artificial intelligence.
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机器学习是人工智能的一个子集。
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## --video-solution--
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@ -1,6 +1,6 @@
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---
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id: 5e8f2f13c4cdbe86b5c72d88
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title: Introduction to TensorFlow
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title: TensorFlow 简介
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challengeType: 11
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videoId: r9hRyGGjOgQ
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dashedName: introduction-to-tensorflow
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@ -10,11 +10,11 @@ dashedName: introduction-to-tensorflow
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## --text--
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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