chore(i18n,curriculum): processed translations (#42868)
This commit is contained in:
camperbot
GitHub
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a0e9ef99a403d019610cc
|
||||
title: Deep Learning Demystified
|
||||
title: 解密深度學習
|
||||
challengeType: 11
|
||||
videoId: bejQ-W9BGJg
|
||||
dashedName: deep-learning-demystified
|
||||
@ -10,23 +10,23 @@ dashedName: deep-learning-demystified
|
||||
|
||||
## --text--
|
||||
|
||||
How should you assign weights to input neurons before training your network for the first time?
|
||||
在你第一次訓練你的網絡之前，你應該如何給輸入層節點分配權重？
|
||||
|
||||
## --answers--
|
||||
|
||||
From smallest to largest.
|
||||
從小到大
|
||||
|
||||
---
|
||||
|
||||
Completely randomly.
|
||||
完全隨機的
|
||||
|
||||
---
|
||||
|
||||
Alphabetically.
|
||||
按字母順序
|
||||
|
||||
---
|
||||
|
||||
None of the above.
|
||||
以上都不對
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a0e9ef99a403d019610cd
|
||||
title: How Convolutional Neural Networks work
|
||||
title: 卷積神經網絡的工作原理
|
||||
challengeType: 11
|
||||
videoId: Y5M7KH4A4n4
|
||||
dashedName: how-convolutional-neural-networks-work
|
||||
@ -10,19 +10,19 @@ dashedName: how-convolutional-neural-networks-work
|
||||
|
||||
## --text--
|
||||
|
||||
When are Convolutional Neural Networks not useful?
|
||||
卷積神經網絡在什麼時候是沒有用的？
|
||||
|
||||
## --answers--
|
||||
|
||||
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.
|
||||
當數據的組成形式不能像圖片存儲的數據格式一樣，或者說你的數據可以重新排列，它仍然可以被運用到卷積神經網絡中。
|
||||
|
||||
---
|
||||
|
||||
If your data is made up of different 2D or 3D images.
|
||||
如果你的數據是由 2D 或者 3D 圖片組成的。
|
||||
|
||||
---
|
||||
|
||||
If your data is text or sound based.
|
||||
如果你的數據是基於文本或者音頻的形式。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a0e9ef99a403d019610ca
|
||||
title: How Deep Neural Networks Work
|
||||
title: 深度神經網絡的工作原理
|
||||
challengeType: 11
|
||||
videoId: zvalnHWGtx4
|
||||
dashedName: how-deep-neural-networks-work
|
||||
@ -10,19 +10,19 @@ dashedName: how-deep-neural-networks-work
|
||||
|
||||
## --text--
|
||||
|
||||
Why is it better to calculate the gradient (slope) directly rather than numerically?
|
||||
相比較數字的計算，爲什麼深度神經網絡可以更好地計算梯度（斜率）？
|
||||
|
||||
## --answers--
|
||||
|
||||
It is computationally expensive to go back through the entire neural network and adjust the weights for each layer of the neural network.
|
||||
通過回溯整個神經網絡來更改每一層神經網絡的權重，在計算上來說是非常耗時的。
|
||||
|
||||
---
|
||||
|
||||
It is more accurate.
|
||||
它更加準確。
|
||||
|
||||
---
|
||||
|
||||
There is no difference between the two methods.
|
||||
這兩種方法之間沒有區別。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a0e9ef99a403d019610cb
|
||||
title: Recurrent Neural Networks RNN and Long Short Term Memory LSTM
|
||||
title: 循環神經網絡 RNN 和長短期記憶 LSTM
|
||||
challengeType: 11
|
||||
videoId: UVimlsy9eW0
|
||||
dashedName: recurrent-neural-networks-rnn-and-long-short-term-memory-lstm
|
||||
@ -10,19 +10,19 @@ dashedName: recurrent-neural-networks-rnn-and-long-short-term-memory-lstm
|
||||
|
||||
## --text--
|
||||
|
||||
What are the main neural network components that make up a Long Short Term Memory network?
|
||||
構成長短期記憶網絡的主要神經網絡組件是什麼？
|
||||
|
||||
## --answers--
|
||||
|
||||
New information and prediction.
|
||||
新的信息和預測
|
||||
|
||||
---
|
||||
|
||||
Prediction, collected possibilities, and selection.
|
||||
預測、收集的可能性和選擇
|
||||
|
||||
---
|
||||
|
||||
Prediction, ignoring, forgetting, and selection.
|
||||
預測、忽視、遺忘和選擇
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,25 +1,26 @@
|
||||
---
|
||||
id: 5e46f8e3ac417301a38fb92f
|
||||
title: Book Recommendation Engine using KNN
|
||||
title: 基於 KNN 的圖書推薦引擎
|
||||
challengeType: 10
|
||||
forumTopicId: 462378
|
||||
dashedName: book-recommendation-engine-using-knn
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
In this challenge, you will create a book recommendation algorithm using K-Nearest Neighbors.
|
||||
在這個挑戰中，你將使用 K-近鄰算法創建一個書籍推薦算法。
|
||||
|
||||
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.
|
||||
你將使用 Book-Crossings 數據集。 該數據集包括 90,000 名用戶對 270,000 冊書籍的 110 萬份評分（評分從 1至 10）。
|
||||
|
||||
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).
|
||||
你可以[在 Replit 上查看整個項目的具體描述和初始代碼](https://colab.research.google.com/github/freeCodeCamp/boilerplate-book-recommendation-engine/blob/master/fcc_book_recommendation_knn.ipynb)。
|
||||
|
||||
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."
|
||||
進入該鏈接後，在你自己的賬戶或本地創建一個筆記本的副本。 一旦你完成項目並通過鏈接中的測試，請在下面提交你的項目鏈接。 如果你提交的是 Google Colaboratory 的鏈接，請確保打開鏈接共享時選擇 “anyone with the link”。
|
||||
|
||||
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.
|
||||
我們仍在開發機器學習課程的交互式課程部分。 現在，你可以通過這個認證視頻挑戰。 你可能還需要尋找額外的學習資源，類似於你在現實世界項目上所做的工作。
|
||||
|
||||
# --hints--
|
||||
|
||||
It should pass all Python tests.
|
||||
它應該通過所有的 Python 測試。
|
||||
|
||||
```js
|
||||
|
||||
|
||||
@ -1,23 +1,24 @@
|
||||
---
|
||||
id: 5e46f8dcac417301a38fb92e
|
||||
title: Cat and Dog Image Classifier
|
||||
title: 貓狗圖像分類器
|
||||
challengeType: 10
|
||||
forumTopicId: 462377
|
||||
dashedName: cat-and-dog-image-classifier
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
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.
|
||||
在這個挑戰中，你將使用 TensorFlow 2.0 和 Keras 創建一個卷積神經網絡，對貓和狗的圖像進行正確分類，準確率至少達到 63%。
|
||||
|
||||
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).
|
||||
你可以訪問 [Google Colaboratory 上的完整項目說明和啓動代碼](https://colab.research.google.com/github/freeCodeCamp/boilerplate-cat-and-dog-image-classifier/blob/master/fcc_cat_dog.ipynb)。
|
||||
|
||||
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."
|
||||
進入該鏈接後，在你自己的賬戶或本地創建一個筆記本的副本。 一旦你完成項目並通過鏈接中的測試，請在下面提交你的項目鏈接。 如果你提交的是 Google Colaboratory 的鏈接，請確保打開鏈接共享時選擇 “anyone with the link”。
|
||||
|
||||
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.
|
||||
我們仍在開發機器學習課程的交互式課程部分。 現在，你可以通過這個認證視頻挑戰。 你可能還需要尋找額外的學習資源，類似於你在真實世界項目中的工作。
|
||||
|
||||
# --hints--
|
||||
|
||||
It should pass all Python tests.
|
||||
它應該通過所有的 Python 測試。
|
||||
|
||||
```js
|
||||
|
||||
|
||||
@ -1,25 +1,26 @@
|
||||
---
|
||||
id: 5e46f8edac417301a38fb930
|
||||
title: Linear Regression Health Costs Calculator
|
||||
title: 線性迴歸健康成本計算器
|
||||
challengeType: 10
|
||||
forumTopicId: 462379
|
||||
dashedName: linear-regression-health-costs-calculator
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
In this challenge, you will predict healthcare costs using a regression algorithm.
|
||||
在這個挑戰中，你將使用迴歸算法預測醫療費用。
|
||||
|
||||
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.
|
||||
你會得到一個數據集，其中包含不同人的信息，包括他們的醫療費用。 用數據來預測基於新數據的醫療費用。
|
||||
|
||||
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).
|
||||
你可以訪問 [Google Colaboratory 上的完整項目說明和啓動代碼](https://colab.research.google.com/github/freeCodeCamp/boilerplate-linear-regression-health-costs-calculator/blob/master/fcc_predict_health_costs_with_regression.ipynb)。
|
||||
|
||||
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."
|
||||
進入該鏈接後，在你自己的賬戶或本地創建一個筆記本的副本。 一旦你完成項目並通過鏈接中的測試，請在下面提交你的項目鏈接。 如果你提交的是 Google Colaboratory 的鏈接，請確保打開鏈接共享時選擇 “anyone with the link”。
|
||||
|
||||
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.
|
||||
我們仍在開發機器學習課程的交互式課程部分。 現在，你可以通過這個認證中的視頻挑戰。 你可能還需要尋找額外的學習資源，類似於你在真實世界項目中的工作。
|
||||
|
||||
# --hints--
|
||||
|
||||
It should pass all Python tests.
|
||||
它應該通過所有的 Python 測試。
|
||||
|
||||
```js
|
||||
|
||||
|
||||
@ -1,23 +1,24 @@
|
||||
---
|
||||
id: 5e46f8edac417301a38fb931
|
||||
title: Neural Network SMS Text Classifier
|
||||
title: 神經網絡短信文本分類器
|
||||
challengeType: 10
|
||||
forumTopicId: 462380
|
||||
dashedName: neural-network-sms-text-classifier
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
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.
|
||||
在這個挑戰中，你需要創建一個機器學習模型，將短信分類爲 “ham” 或 “spam”。 “ham” 信息是由一個朋友發送的普通信息。 “spam” 是一個公司發送的廣告或信息。
|
||||
|
||||
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).
|
||||
你可以訪問 [Google Colaboratory 上的完整項目說明和啓動代碼](https://colab.research.google.com/github/freeCodeCamp/boilerplate-neural-network-sms-text-classifier/blob/master/fcc_sms_text_classification.ipynb)。
|
||||
|
||||
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."
|
||||
進入該鏈接後，在你自己的賬戶或本地創建一個筆記本的副本。 一旦你完成項目並通過鏈接中的測試，請在下面提交你的項目鏈接。 如果你提交的是 Google Colaboratory 的鏈接，請確保打開鏈接共享時選擇 “anyone with the link”。
|
||||
|
||||
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.
|
||||
我們仍在開發機器學習課程的交互式課程部分。 現在，你可以通過這個認證中的視頻挑戰。 你可能還需要尋找額外的學習資源，類似於你在真實世界項目中的工作。
|
||||
|
||||
# --hints--
|
||||
|
||||
It should pass all Python tests.
|
||||
它應該通過所有的 Python 測試。
|
||||
|
||||
```js
|
||||
|
||||
|
||||
@ -1,23 +1,24 @@
|
||||
---
|
||||
id: 5e46f8d6ac417301a38fb92d
|
||||
title: Rock Paper Scissors
|
||||
title: 剪刀石頭布
|
||||
challengeType: 10
|
||||
forumTopicId: 462376
|
||||
dashedName: rock-paper-scissors
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
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.
|
||||
在這個挑戰中，你將創建一個程序來玩石頭、剪刀、布。 一個隨機選取的程序通常會有 50% 的時間獲勝。 要通過這一挑戰，你的程序必須與四個不同的機器人進行對戰，並達到至少 60% 勝率。
|
||||
|
||||
You can access [the full project description and starter code on repl.it](https://repl.it/github/freeCodeCamp/boilerplate-rock-paper-scissors).
|
||||
你可以訪問 [Replit 上的完整項目描述和啓動代碼](https://replit.com/github/freeCodeCamp/boilerplate-rock-paper-scissors)。
|
||||
|
||||
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.
|
||||
進入該鏈接後，fork 該項目。 一旦你根據 “README.md” 中的說明完成了項目，請在下面提交你的項目鏈接。
|
||||
|
||||
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.
|
||||
我們仍在開發機器學習課程的交互式課程部分。 現在，你需要使用其他資源來學習如何通過這一挑戰。
|
||||
|
||||
# --hints--
|
||||
|
||||
It should pass all Python tests.
|
||||
它應該通過所有的 Python 測試。
|
||||
|
||||
```js
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72da6
|
||||
title: Conclusion
|
||||
title: 結論
|
||||
challengeType: 11
|
||||
videoId: LMNub5frQi4
|
||||
dashedName: conclusion
|
||||
@ -10,19 +10,19 @@ dashedName: conclusion
|
||||
|
||||
## --text--
|
||||
|
||||
Most people that are experts in AI or machine learning usually...:
|
||||
大多數人工智能或機器學習專家通常......
|
||||
|
||||
## --answers--
|
||||
|
||||
have one specialization.
|
||||
有一個專業。
|
||||
|
||||
---
|
||||
|
||||
have many specializations.
|
||||
有很多專業。
|
||||
|
||||
---
|
||||
|
||||
have a deep understanding of many different frameworks.
|
||||
對許多不同的框架有深入的瞭解。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d99
|
||||
title: 'Convolutional Neural Networks: Evaluating the Model'
|
||||
title: '卷積神經網絡：評估模型'
|
||||
challengeType: 11
|
||||
videoId: eCATNvwraXg
|
||||
dashedName: convolutional-neural-networks-evaluating-the-model
|
||||
@ -10,19 +10,19 @@ dashedName: convolutional-neural-networks-evaluating-the-model
|
||||
|
||||
## --text--
|
||||
|
||||
What is **not** a good way to increase the accuracy of a convolutional neural network?
|
||||
什麼 **不是** 提高卷積神經網絡準確性的好方法？
|
||||
|
||||
## --answers--
|
||||
|
||||
Augmenting the data you already have.
|
||||
擴充你已有的數據。
|
||||
|
||||
---
|
||||
|
||||
Using a pre-trained model.
|
||||
使用預先訓練的模型。
|
||||
|
||||
---
|
||||
|
||||
Using your test data to retrain the model.
|
||||
使用你的測試數據重新訓練模型。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d9a
|
||||
title: 'Convolutional Neural Networks: Picking a Pretrained Model'
|
||||
title: '卷積神經網絡：選擇預訓練模型'
|
||||
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:
|
||||
填寫下面的空白，使用谷歌預訓練的 MobileNet V2 模型作爲卷積神經網絡的基礎：
|
||||
|
||||
```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: '卷積神經網絡：卷積層'
|
||||
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?
|
||||
每個卷積層的三個主要屬性是什麼？
|
||||
|
||||
## --answers--
|
||||
|
||||
Input size, the number of filters, and the sample size of the filters.
|
||||
輸入大小、過濾器數量和過濾器的樣本大小。
|
||||
|
||||
---
|
||||
|
||||
Input size, input dimensions, and the color values of the input.
|
||||
輸入大小、輸入尺寸和輸入的顏色值。
|
||||
|
||||
---
|
||||
|
||||
Input size, input padding, and stride.
|
||||
輸入大小、輸入填充和步長。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d96
|
||||
title: Convolutional Neural Networks
|
||||
title: 卷積神經網絡
|
||||
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...:
|
||||
密集神經網絡在全局範圍內分析輸入，並識別特定區域的模式。 卷積神經網絡......：
|
||||
|
||||
## --answers--
|
||||
|
||||
also analyze input globally and extract features from specific areas.
|
||||
也在全局分析輸入並從特定區域提取特徵。
|
||||
|
||||
---
|
||||
|
||||
do not work well for image classification or object detection.
|
||||
在圖像分類或物體檢測方面效果不佳。
|
||||
|
||||
---
|
||||
|
||||
scan through the entire input a little at a time and learn local patterns.
|
||||
每次一點點地掃描整個輸入，並學習局部模式。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d8e
|
||||
title: 'Core Learning Algorithms: Building the Model'
|
||||
title: '核心學習算法：構建模型'
|
||||
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 推薦使用哪種估計器/模型進行分類？
|
||||
|
||||
## --answers--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d8d
|
||||
title: 'Core Learning Algorithms: Classification'
|
||||
title: '核心學習算法：分類'
|
||||
challengeType: 11
|
||||
videoId: qFF7ZQNvK9E
|
||||
dashedName: core-learning-algorithms-classification
|
||||
@ -10,19 +10,19 @@ dashedName: core-learning-algorithms-classification
|
||||
|
||||
## --text--
|
||||
|
||||
What is classification?
|
||||
什麼是分類？
|
||||
|
||||
## --answers--
|
||||
|
||||
The process of separating data points into different classes.
|
||||
將數據點分離成不同類別的過程。
|
||||
|
||||
---
|
||||
|
||||
Predicting a numeric value or forecast based on independent and dependent variables.
|
||||
根據自變量和因變量預測數值或預測。
|
||||
|
||||
---
|
||||
|
||||
None of the above.
|
||||
以上都不是。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d8f
|
||||
title: 'Core Learning Algorithms: Clustering'
|
||||
title: '核心學習算法：聚類'
|
||||
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?
|
||||
以下哪個步驟 **不是** K-Means 算法的一部分？
|
||||
|
||||
## --answers--
|
||||
|
||||
Randomly pick K points to place K centeroids.
|
||||
隨機選取 K 個點放置 K 個質心。
|
||||
|
||||
---
|
||||
|
||||
Assign each K point to the closest K centeroid.
|
||||
將每個 K 點分配給最近的 K 質心。
|
||||
|
||||
---
|
||||
|
||||
Move each K centeroid into the middle of all of their data points.
|
||||
將每個 K 質心移動到其所有數據點的中間。
|
||||
|
||||
---
|
||||
|
||||
Shuffle the K points so they're redistributed randomly.
|
||||
打亂 K 點，使它們隨機重新分配。
|
||||
|
||||
---
|
||||
|
||||
Reassign each K point to the closest K centeroid.
|
||||
重新分配每個 K 點給最近的 K 質心。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d90
|
||||
title: 'Core Learning Algorithms: Hidden Markov Models'
|
||||
title: '核心學習算法：隱馬爾可夫模型'
|
||||
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?
|
||||
隱馬爾科夫模型與線性迴歸或分類有何不同？
|
||||
|
||||
## --answers--
|
||||
|
||||
It uses probability distributions to predict future events or states.
|
||||
它使用概率分佈來預測未來的事件或狀態。
|
||||
|
||||
---
|
||||
|
||||
It analyzes the relationship between independent and dependent variables to make predictions.
|
||||
它分析自變量和因變量之間的關係以進行預測。
|
||||
|
||||
---
|
||||
|
||||
It separates data points into separate categories.
|
||||
它將數據點分成不同的類別。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d8c
|
||||
title: 'Core Learning Algorithms: The Training Process'
|
||||
title: '核心學習算法：訓練過程'
|
||||
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?
|
||||
什麼是 epoch？
|
||||
|
||||
## --answers--
|
||||
|
||||
The number of times the model will see the same data.
|
||||
模型看到相同數據的次數。
|
||||
|
||||
---
|
||||
|
||||
A type of graph.
|
||||
一種圖。
|
||||
|
||||
---
|
||||
|
||||
The number of elements you feed to the model at once.
|
||||
你一次提供給模型的元素數量。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d8b
|
||||
title: 'Core Learning Algorithms: Training and Testing Data'
|
||||
title: '核心學習算法：訓練和測試數據'
|
||||
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?
|
||||
什麼是分類數據？
|
||||
|
||||
## --answers--
|
||||
|
||||
Another term for one-hot encoding.
|
||||
獨熱編碼的另一個術語。
|
||||
|
||||
---
|
||||
|
||||
Any data that is not numeric.
|
||||
任何非數字的數據。
|
||||
|
||||
---
|
||||
|
||||
Any data that is represented numerically.
|
||||
任何以數字表示的數據。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d91
|
||||
title: 'Core Learning Algorithms: Using Probabilities to make Predictions'
|
||||
title: '核心學習算法：使用概率進行預測'
|
||||
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()`?
|
||||
你應該導入什麼 TensorFlow 模塊來實現 `.HiddenMarkovModel()`？
|
||||
|
||||
## --answers--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d8a
|
||||
title: 'Core Learning Algorithms: Working with Data'
|
||||
title: '核心學習算法：處理數據'
|
||||
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()` 函數有什麼作用？
|
||||
|
||||
## --answers--
|
||||
|
||||
Returns the number of entries in a data frame.
|
||||
返回數據框中的條目數。
|
||||
|
||||
---
|
||||
|
||||
Returns the number of columns in a data frame.
|
||||
返回數據框中的列數。
|
||||
|
||||
---
|
||||
|
||||
By default, shows the first five rows or entries in a data frame.
|
||||
默認情況下，顯示數據框中的前五行或條目。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d89
|
||||
title: Core Learning Algorithms
|
||||
title: 核心學習算法
|
||||
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?:
|
||||
哪種類型的分析最適合以下問題？
|
||||
|
||||
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.
|
||||
你擁有過去 100 年三月份的平均溫度。 使用此數據，你希望預測 5 年後 3 月的平均溫度。
|
||||
|
||||
## --answers--
|
||||
|
||||
Multiple regression
|
||||
多重回歸
|
||||
|
||||
---
|
||||
|
||||
Correlation
|
||||
關連
|
||||
|
||||
---
|
||||
|
||||
Decision tree
|
||||
決策樹
|
||||
|
||||
---
|
||||
|
||||
Linear regression
|
||||
線性迴歸
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d98
|
||||
title: Creating a Convolutional Neural Network
|
||||
title: 創建卷積神經網絡
|
||||
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:
|
||||
填寫下面的空白以完成卷積神經網絡的架構：
|
||||
|
||||
```py
|
||||
model = models.__A__()
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d87
|
||||
title: 'Introduction: Machine Learning Fundamentals'
|
||||
title: '簡介：機器學習基礎'
|
||||
challengeType: 11
|
||||
videoId: KwL1qTR5MT8
|
||||
dashedName: introduction-machine-learning-fundamentals
|
||||
@ -10,19 +10,19 @@ dashedName: introduction-machine-learning-fundamentals
|
||||
|
||||
## --text--
|
||||
|
||||
Which statement below is **false**?
|
||||
以下哪個陳述是 **假的**？
|
||||
|
||||
## --answers--
|
||||
|
||||
Neural networks are modeled after the way the human brain works.
|
||||
神經網絡以人腦的工作方式爲模型。
|
||||
|
||||
---
|
||||
|
||||
Computer programs that play tic-tac-toe or chess against human players are examples of simple artificial intelligence.
|
||||
與人類玩家玩井字棋或國際象棋的計算機程序是簡單人工智能的例子。
|
||||
|
||||
---
|
||||
|
||||
Machine learning is a subset of artificial intelligence.
|
||||
機器學習是人工智能的一個子集。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d88
|
||||
title: Introduction to TensorFlow
|
||||
title: TensorFlow 簡介
|
||||
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?
|
||||
以下哪個 **不是** 張量的類型？
|
||||
|
||||
## --answers--
|
||||
|
||||
Variable
|
||||
變量
|
||||
|
||||
---
|
||||
|
||||
@ -22,15 +22,15 @@ Flowing
|
||||
|
||||
---
|
||||
|
||||
Placeholder
|
||||
佔位符
|
||||
|
||||
---
|
||||
|
||||
SparseTensor
|
||||
稀疏張量
|
||||
|
||||
---
|
||||
|
||||
Constant
|
||||
常量
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72da1
|
||||
title: 'Natural Language Processing With RNNs: Building the Model'
|
||||
title: '使用 RNN 處理自然語言：構建模型'
|
||||
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:
|
||||
填寫下面的空白以完成 `build_model` 函數：
|
||||
|
||||
```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 進行自然語言處理：創建戲劇生成器'
|
||||
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:
|
||||
填寫下面的空白以創建 RNN 的訓練示例：
|
||||
|
||||
```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 進行自然語言處理：進行預測'
|
||||
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...:
|
||||
在你通過自己的評論做出預測之前，你應該：
|
||||
|
||||
## --answers--
|
||||
|
||||
decode the training dataset and compare the results to the test data.
|
||||
對訓練數據集進行解碼，並將結果與測試數據進行比較。
|
||||
|
||||
---
|
||||
|
||||
use the encodings from the training dataset to encode your review.
|
||||
使用訓練數據集的編碼對你的評論進行編碼。
|
||||
|
||||
---
|
||||
|
||||
assign random values between 0 and the maximum number of vocabulary in your dataset to each word in your review.
|
||||
將 0 和數據集中最大詞彙量之間的值，隨機分配給你的評論中的每個單詞。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d9c
|
||||
title: 'Natural Language Processing With RNNs: Part 2'
|
||||
title: '使用 RNN 進行自然語言處理：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...:
|
||||
詞嵌入是指...：
|
||||
|
||||
## --answers--
|
||||
|
||||
an unordered group of encoded words that describes the frequency of words in a given document.
|
||||
一組無序的編碼詞，描述給定文檔中詞的頻率。
|
||||
|
||||
---
|
||||
|
||||
a group of encoded words that preserves the original order of the words in a given document.
|
||||
一組編碼單詞，保留給定文檔中單詞的原始順序。
|
||||
|
||||
---
|
||||
|
||||
a vectorized representation of words in a given document that places words with similar meanings near each other.
|
||||
給定文檔中單詞的矢量化表示，將具有相似含義的單詞彼此靠近。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d9d
|
||||
title: 'Natural Language Processing With RNNs: Recurring Neural Networks'
|
||||
title: '使用 RNN 進行自然語言處理：循環神經網絡'
|
||||
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?
|
||||
關於循環神經網絡，哪一項是正確的？
|
||||
|
||||
## --answers--
|
||||
|
||||
1: They are a type of feed-forward neural network.
|
||||
1：它們是一種前饋神經網絡。
|
||||
|
||||
---
|
||||
|
||||
2: They maintain an internal memory/state of the input that was already processed.
|
||||
2：它們保持着一個已經處理過的輸入的內部存儲器/狀態。
|
||||
|
||||
---
|
||||
|
||||
3: RNN's contain a loop and process one piece of input at a time.
|
||||
3：RNN 包含一個循環，每次處理一個輸入。
|
||||
|
||||
---
|
||||
|
||||
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 進行自然語言處理：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:
|
||||
填寫下面的空白來創建 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 進行自然語言處理：訓練模型'
|
||||
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:
|
||||
填寫下面的空白以將你模型的檢查點保存在 `./checkpoints` 目錄中，並調用最新的檢查點進行訓練：
|
||||
|
||||
```py
|
||||
checkpoint_dir = __A__
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d9b
|
||||
title: Natural Language Processing With RNNs
|
||||
title: 使用 RNN 的自然語言處理
|
||||
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...:
|
||||
自然語言處理是人工智能的一個分支......：
|
||||
|
||||
## --answers--
|
||||
|
||||
deals with how computers understand and process natural/human languages.
|
||||
解決計算機如何理解和處理自然語言/人文語言。
|
||||
|
||||
---
|
||||
|
||||
translates image data into natural/human languages.
|
||||
將圖像數據轉換成自然/人文語言。
|
||||
|
||||
---
|
||||
|
||||
is focused on translating computer languages into natural/human languages.
|
||||
重點是將計算機語言翻譯成自然/人文語言。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d93
|
||||
title: 'Neural Networks: Activation Functions'
|
||||
title: '神經網絡：激活函數'
|
||||
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?
|
||||
哪個激活函數在 -1 和 1 之間切換值？
|
||||
|
||||
## --answers--
|
||||
|
||||
ReLU (Rectified Linear Unit)
|
||||
ReLU（線性整流函數）
|
||||
|
||||
---
|
||||
|
||||
Tanh (Hyperbolic Tangent)
|
||||
Tanh（雙曲函數）
|
||||
|
||||
---
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d95
|
||||
title: 'Neural Networks: Creating a Model'
|
||||
title: '神經網絡：創建模型'
|
||||
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:
|
||||
填寫下面的空白，建立一個密集層的順序模型。
|
||||
|
||||
```py
|
||||
model = __A__.__B__([
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d94
|
||||
title: 'Neural Networks: Optimizers'
|
||||
title: '神經網絡：優化'
|
||||
challengeType: 11
|
||||
videoId: hdOtRPQe1o4
|
||||
dashedName: neural-networks-optimizers
|
||||
@ -10,19 +10,19 @@ dashedName: neural-networks-optimizers
|
||||
|
||||
## --text--
|
||||
|
||||
What is an optimizer function?
|
||||
什麼是優化函數？
|
||||
|
||||
## --answers--
|
||||
|
||||
A function that increases the accuracy of a model's predictions.
|
||||
一種提高模型預測準確性的函數。
|
||||
|
||||
---
|
||||
|
||||
A function that implements the gradient descent and backpropagation algorithms for you.
|
||||
一個爲你實現梯度下降和反向傳播算法的函數。
|
||||
|
||||
---
|
||||
|
||||
A function that reduces the time a model needs to train.
|
||||
一個可以減少模型訓練時間的函數。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d92
|
||||
title: Neural Networks with TensorFlow
|
||||
title: 使用 TensorFlow 的神經網絡
|
||||
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...:
|
||||
一個密集連接的神經網絡是這樣一種網絡：
|
||||
|
||||
## --answers--
|
||||
|
||||
all the neurons in the current layer are connected to one neuron in the previous layer.
|
||||
當前層中的所有神經元都連接到前一層中的一個神經元。
|
||||
|
||||
---
|
||||
|
||||
all the neurons in each layer are connected randomly.
|
||||
每層的所有神經元都是隨機連接的。
|
||||
|
||||
---
|
||||
|
||||
all the neurons in the current layer are connected to every neuron in the previous layer.
|
||||
當前層中的所有神經元都連接到前一層中的每個神經元。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72da5
|
||||
title: 'Reinforcement Learning With Q-Learning: Example'
|
||||
title: '使用 Q-Learning 進行強化學習：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 進行強化學習：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?
|
||||
如果智能體在採取隨機動作和使用學習動作之間沒有很好的平衡，會發生什麼？
|
||||
|
||||
## --answers--
|
||||
|
||||
The agent will always try to minimize its reward for the current state/action, leading to local minima.
|
||||
智能體將始終嘗試將其對當前狀態/動作的獎勵最小化，從而導致局部最小值。
|
||||
|
||||
---
|
||||
|
||||
The agent will always try to maximize its reward for the current state/action, leading to local maxima.
|
||||
智能體將始終嘗試將其對當前狀態/動作的獎勵最大化，從而導致局部最大值。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72da3
|
||||
title: Reinforcement Learning With Q-Learning
|
||||
title: 使用 Q-Learning 進行強化學習
|
||||
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...
|
||||
強化學習的關鍵組成部分是......
|
||||
|
||||
## --answers--
|
||||
|
||||
environment, representative, state, reaction, and reward.
|
||||
環境、代表、狀態、反應和獎勵。
|
||||
|
||||
---
|
||||
|
||||
environment, agent, state, action, and reward.
|
||||
環境、代理、狀態、動作和獎勵。
|
||||
|
||||
---
|
||||
|
||||
habitat, agent, state, action, and punishment.
|
||||
環境、代理、狀態、動作和懲罰。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
Reference in New Issue
Block a user