提交 b63f9247 编写于 作者: Y Yi Wang

Remove unused figure translation marks in fit_a_line/README.en.md

上级 6fbdbe1a
......@@ -14,7 +14,7 @@ where $\omega_{d}$ and $b$ are the model parameters we want to estimate. Once th
We first show the training result of our model. We use the [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) to train a linear model and predict the house prices in Boston. The figure below shows the predictions the model makes for some house prices. The $X$ coordinate of each point represents the median value of the prices of a certain type of houses, while the $Y$ coordinate represents the predicted value by our linear model. When $X=Y$, the point lies exactly on the dotted line. In other words, the more precise the model predicts, the closer the point is to the dotted line.
<p align="center">
<img src = "image/predictions_en.png" width=400><br/>
Figure 1. Predicted Value V.S. Actual Value (波士顿房价预测->Prediction of Boston house prices; 预测价格->Predicted prices; 单位->Units; 实际价格->Actual prices)
Figure 1. Predicted Value V.S. Actual Value
</p>
## Model Overview
......@@ -86,7 +86,7 @@ There are at least three reasons for [Feature Normalization](https://en.wikipedi
<p align="center">
<img src = "image/ranges_en.png" width=550><br/>
Figure 2. The value ranges of the features (特征尺度->Feature value range)
Figure 2. The value ranges of the features
</p>
#### Prepare Training and Test Sets
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