update intro and mlp

scape1989 · Feb 17, 2021 · d920c2c · d920c2c
1 parent c700faa
commit d920c2c
Show file tree

Hide file tree

Showing 39 changed files with 18 additions and 6,956 deletions.
diff --git a/chapter_introduction/index.md b/chapter_introduction/index.md
@@ -176,7 +176,7 @@ $$l(y, y') = \sum_i (y_i - y_i')^2.$$
 
 请注意，最有可能的类不一定是您要用于决策的类。假设你发现这个美丽的蘑菇在你的后院如 :numref:`fig_death_cap` 所示。
 
-![Death cap---do not eat!](../img/death_cap.jpg)
+![Death cap---do not eat!](../img/death-cap.jpg)
 :width:`200px`
 :label:`fig_death_cap`
 
@@ -221,7 +221,7 @@ $$L(\mathrm{action}| x) = E_{y \sim p(y| x)}[\mathrm{loss}(\mathrm{action},y)].$
 
 鉴于这样的模型，那么对于任何给定的用户，我们可以检索分数最大 $y_{ij}$ 的对象集，然后可以推荐给客户。生产系统比较先进，并且在计算此类分数时考虑详细的用户活动和项目特征。:numref:`fig_deeplearning_amazon` 是亚马逊推荐的深度学习书籍样本，基于个性化算法，经过调整，以捕捉作者的偏好。
 
-![Deep learning books recommended by Amazon.](../img/deeplearning_amazon.jpg)
+![Deep learning books recommended by Amazon.](../img/deeplearning-amazon.jpg)
 :label:`fig_deeplearning_amazon`
 
 尽管推荐系统具有巨大的经济价值，但天真地建立在预测模型之上，仍然存在一些严重的概念缺陷。首先，我们只观察 * 审查反馈 *。用户优先评分他们感觉强烈的电影：您可能会注意到项目获得了许多 5 星和 1 星评级，但三星评级显然很少。此外，当前的购买习惯往往是目前使用的推荐算法的结果，但学习算法并不总是考虑到这一细节。因此，反馈循环有可能形成，推荐人系统会优先推荐一个项目，然后由于购买量增加），反过来更频繁地推荐一个项目。许多关于如何处理审查、激励和反馈循环的问题都是重要的开放研究问题。

diff --git a/chapter_introduction/index_origin.md b/chapter_introduction/index_origin.md
@@ -682,7 +682,7 @@ the one that you are going to use for your decision.
 Assume that you find this beautiful mushroom in your backyard
 as shown in :numref:`fig_death_cap`.
 
-![Death cap---do not eat!](../img/death_cap.jpg)
+![Death cap---do not eat!](../img/death-cap.jpg)
 :width:`200px`
 :label:`fig_death_cap`
 
@@ -852,7 +852,7 @@ detailed user activity and item characteristics into account
 when computing such scores. :numref:`fig_deeplearning_amazon` is an example
 of deep learning books recommended by Amazon based on personalization algorithms tuned to capture the author's preferences.
 
-![Deep learning books recommended by Amazon.](../img/deeplearning_amazon.jpg)
+![Deep learning books recommended by Amazon.](../img/deeplearning-amazon.jpg)
 :label:`fig_deeplearning_amazon`
 
 Despite their tremendous economic value, recommendation systems

diff --git a/chapter_linear-networks/linear-regression.md b/chapter_linear-networks/linear-regression.md
@@ -57,7 +57,7 @@ $$l^{(i)}(\mathbf{w}, b) = \frac{1}{2} \left(\hat{y}^{(i)} - y^{(i)}\right)^2.$$
 
 这个常数$\frac{1}{2}$不会带来本质的差别，但这样在形式上稍微简单一些。当我们对损失函数求导后常数系数为1。由于训练数据集是给我们的，并不受我们控制，所以经验误差只是关于模型参数的函数。为了进一步说明，来看下面的例子，我们为一维情况下的回归问题绘制图像，如 :numref:`fig_fit_linreg` 所示。
 
-![用线性模型拟合数据。](../img/fit_linreg.svg)
+![用线性模型拟合数据。](../img/fit-linreg.svg)
 :label:`fig_fit_linreg`
 
 由于平方误差函数中的二次方项，估计值 $\hat{y}^{(i)}$ 和观测值 $y^{(i)}$ 之间较大的差异将贡献更大的损失。为了度量模型在整个数据集上的质量，我们需计算在训练集$n$个样本上的损失均值（也等价于求和）。

diff --git a/chapter_linear-networks/linear-regression_origin.md b/chapter_linear-networks/linear-regression_origin.md
@@ -80,7 +80,7 @@ limit the expressivity of our model.
 Strictly speaking, :eqref:`eq_price-area` is an *affine transformation*
 of input features,
 which is characterized by
-a *linear transformation* of features via weighted sum, combined with 
+a *linear transformation* of features via weighted sum, combined with
 a *translation* via the added bias.
 
 Given a dataset, our goal is to choose
@@ -177,7 +177,7 @@ To make things more concrete, consider the example below
 where we plot a regression problem for a one-dimensional case
 as shown in :numref:`fig_fit_linreg`.
 
-![Fit data with a linear model.](../img/fit_linreg.svg)
+![Fit data with a linear model.](../img/fit-linreg.svg)
 :label:`fig_fit_linreg`
 
 Note that large differences between

diff --git a/chapter_multilayer-perceptrons/kaggle-house-price.md b/chapter_multilayer-perceptrons/kaggle-house-price.md
@@ -84,7 +84,7 @@ def download_all():  #@save
 
 > https://www.kaggle.com/c/house-prices-advanced-regression-techniques
 
-![The house price prediction competition page.](../img/house_pricing.png)
+![The house price prediction competition page.](../img/house-pricing.png)
 :width:`400px`
 :label:`fig_house_pricing`
 
@@ -448,7 +448,7 @@ train_and_pred(train_features, test_features, train_labels, test_data,
 * 点击页面底部虚线框中的 “上传提交文件” 按钮，然后选择要上传的预测文件。
 * 点击页面底部的 “提交” 按钮查看您的结果。
 
-![Submitting data to Kaggle](../img/kaggle_submit2.png)
+![Submitting data to Kaggle](../img/kaggle-submit2.png)
 :width:`400px`
 :label:`fig_kaggle_submit2`
 

diff --git a/chapter_multilayer-perceptrons/kaggle-house-price_origin.md b/chapter_multilayer-perceptrons/kaggle-house-price_origin.md
@@ -142,7 +142,7 @@ The URL is right here:
 
 > https://www.kaggle.com/c/house-prices-advanced-regression-techniques
 
-![The house price prediction competition page.](../img/house_pricing.png)
+![The house price prediction competition page.](../img/house-pricing.png)
 :width:`400px`
 :label:`fig_house_pricing`
 
@@ -313,7 +313,7 @@ in the same way that we previously transformed
 multiclass labels into vectors (see :numref:`subsec_classification-problem`).
 For instance, "MSZoning" assumes the values "RL" and "RM".
 Dropping the "MSZoning" feature,
-two new indicator features 
+two new indicator features
 "MSZoning_RL" and "MSZoning_RM" are created with values being either 0 or 1.
 According to one-hot encoding,
 if the original value of "MSZoning" is "RL",
@@ -664,7 +664,7 @@ The steps are quite simple:
 * Click the “Upload Submission File” button in the dashed box at the bottom of the page and select the prediction file you wish to upload.
 * Click the “Make Submission” button at the bottom of the page to view your results.
 
-![Submitting data to Kaggle](../img/kaggle_submit2.png)
+![Submitting data to Kaggle](../img/kaggle-submit2.png)
 :width:`400px`
 :label:`fig_kaggle_submit2`
 

diff --git a/chapter_multilayer-perceptrons/underfit-overfit.md b/chapter_multilayer-perceptrons/underfit-overfit.md
@@ -93,7 +93,7 @@ $$\hat{y}= \sum_{i=0}^d x^i w_i$$
 
 高阶多项式函数比低阶多项式函数复杂得多，因为高阶多项式具有更多的参数，而且模型函数的选择范围也更宽。修复训练数据集时，较高阶多项式函数应始终实现相对于较低度多项式的训练误差（在最坏情况下，相等）。事实上，每当每个数据点的不同值为 $x$ 时，度等于数据点数的多项式函数就可以完美地拟合训练集。我们在 :numref:`fig_capacity_vs_error` 中对多项式度与欠拟合与过拟合之间的关系进行了可视化。
 
-![Influence of model complexity on underfitting and overfitting](../img/capacity_vs_error.svg)
+![Influence of model complexity on underfitting and overfitting](../img/capacity-vs-error.svg)
 :label:`fig_capacity_vs_error`
 
 ### 数据集大小

diff --git a/chapter_multilayer-perceptrons/underfit-overfit_origin.md b/chapter_multilayer-perceptrons/underfit-overfit_origin.md
@@ -361,7 +361,7 @@ can fit the training set perfectly.
 We visualize the relationship between polynomial degree
 and underfitting vs. overfitting in :numref:`fig_capacity_vs_error`.
 
-![Influence of model complexity on underfitting and overfitting](../img/capacity_vs_error.svg)
+![Influence of model complexity on underfitting and overfitting](../img/capacity-vs-error.svg)
 :label:`fig_capacity_vs_error`
 
 ### Dataset Size

diff --git a/chapter_preliminaries/calculus.md b/chapter_preliminaries/calculus.md
@@ -4,7 +4,7 @@
 直到至少2500年前，古希腊人把一个多边形分成三角形，并把它们的面积相加，才找到计算多边形面积的方法。
 为了求出曲线形状（比如圆）的面积，古希腊人在这样的形状上刻内接多边形。如 :numref:`fig_circle_area` 所示，内接多边形的等长边越多，就越接近圆。这个过程也被称为*逼近法*（method of exhaustion）。
 
-![用穷举法求圆的面积。](../img/polygon_circle.svg)
+![用穷举法求圆的面积。](../img/polygon-circle.svg)
 :label:`fig_circle_area`
 
 事实上，逼近法就是 *积分*（integral calculus）（将在 :numref:`sec_integral_calculus` 中描述）的起源。2000 多年后，微积分的另一支，*微分*（differential calculus），被发明出来。在微分学最重要的应用中，优化问题考虑如何把事情做到*最好*。正如在 :numref:`subsec_norms_and_objectives` 中讨论的那样，这种问题在深度学习中是无处不在的。

diff --git a/chapter_preliminaries/calculus_origin.md b/chapter_preliminaries/calculus_origin.md
@@ -14,7 +14,7 @@ As shown in :numref:`fig_circle_area`,
 an inscribed polygon with more sides of equal length better approximates
 the circle. This process is also known as the *method of exhaustion*.
 
-![Find the area of a circle with the method of exhaustion.](../img/polygon_circle.svg)
+![Find the area of a circle with the method of exhaustion.](../img/polygon-circle.svg)
 :label:`fig_circle_area`
 
 In fact, the method of exhaustion is where *integral calculus* (will be described in :numref:`sec_integral_calculus`) originates from.

diff --git a/chapter_preliminaries/probability.md b/chapter_preliminaries/probability.md
@@ -8,7 +8,7 @@
 
 在前面的章节中，我们已经提到了概率，但没有明确说明它们是什么，也没有给出具体的例子。现在让我们更认真地考虑第一个例子：根据照片区分猫和狗。这听起来可能很简单，但实际上是一个艰巨的挑战。首先，问题的难度可能取决于图像的分辨率。
 
-![不同分辨率的图像 ($10 \times 10$, $20 \times 20$, $40 \times 40$, $80 \times 80$, 和 $160 \times 160$ pixels).](../img/cat_dog_pixels.png)
+![不同分辨率的图像 ($10 \times 10$, $20 \times 20$, $40 \times 40$, $80 \times 80$, 和 $160 \times 160$ pixels).](../img/cat-dog-pixels.png)
 :width:`300px`
 :label:`fig_cat_dog`
 

diff --git a/chapter_preliminaries/probability_origin.md b/chapter_preliminaries/probability_origin.md
@@ -12,7 +12,7 @@ Entire courses, majors, theses, careers, and even departments, are devoted to pr
 
 We have already invoked probabilities in previous sections without articulating what precisely they are or giving a concrete example. Let us get more serious now by considering the first case: distinguishing cats and dogs based on photographs. This might sound simple but it is actually a formidable challenge. To start with, the difficulty of the problem may depend on the resolution of the image.
 
-![Images of varying resolutions ($10 \times 10$, $20 \times 20$, $40 \times 40$, $80 \times 80$, and $160 \times 160$ pixels).](../img/cat_dog_pixels.png)
+![Images of varying resolutions ($10 \times 10$, $20 \times 20$, $40 \times 40$, $80 \times 80$, and $160 \times 160$ pixels).](../img/cat-dog-pixels.png)
 :width:`300px`
 :label:`fig_cat_dog`