diff --git a/chapter_introduction/index.md b/chapter_introduction/index.md index 7bf012414..901aff149 100644 --- 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 @@ $$L(\mathrm{action}| x) = E_{y \sim p(y| x)}[\mathrm{loss}(\mathrm{action},y)].$ 鉴于这样的模型，那么对于任何给定的用户，我们可以检索分数最大 $y_{ij}$ 的对象集，然后可以推荐给客户。生产系统比较先进，并且在计算此类分数时考虑详细的用户活动和项目特征。:numref:`fig_deeplearning_amazon` 是亚马逊推荐的深度学习书籍样本，基于个性化算法，经过调整，以捕捉作者的偏好。 -![Deep learning books recommended by Amazon.](../img/deeplearning_amazon.jpg) +![Deep learning books recommended by Amazon.](../img/deeplearning-amazon.jpg) :label:`fig_deeplearning_amazon` 尽管推荐系统具有巨大的经济价值，但天真地建立在预测模型之上，仍然存在一些严重的概念缺陷。首先，我们只观察 * 审查反馈 *。用户优先评分他们感觉强烈的电影：您可能会注意到项目获得了许多 5 星和 1 星评级，但三星评级显然很少。此外，当前的购买习惯往往是目前使用的推荐算法的结果，但学习算法并不总是考虑到这一细节。因此，反馈循环有可能形成，推荐人系统会优先推荐一个项目，然后由于购买量增加），反过来更频繁地推荐一个项目。许多关于如何处理审查、激励和反馈循环的问题都是重要的开放研究问题。 diff --git a/chapter_introduction/index_origin.md b/chapter_introduction/index_origin.md index 61e88b926..610d49770 100644 --- a/chapter_introduction/index_origin.md +++ b/chapter_introduction/index_origin.md @@ -682,7 +682,7 @@ the one that you are going to use for your decision. Assume that you find this beautiful mushroom in your backyard as shown in :numref:`fig_death_cap`. -![Death cap---do not eat!](../img/death_cap.jpg) +![Death cap---do not eat!](../img/death-cap.jpg) :width:`200px` :label:`fig_death_cap` @@ -852,7 +852,7 @@ detailed user activity and item characteristics into account when computing such scores. :numref:`fig_deeplearning_amazon` is an example of deep learning books recommended by Amazon based on personalization algorithms tuned to capture the author's preferences. -![Deep learning books recommended by Amazon.](../img/deeplearning_amazon.jpg) +![Deep learning books recommended by Amazon.](../img/deeplearning-amazon.jpg) :label:`fig_deeplearning_amazon` Despite their tremendous economic value, recommendation systems diff --git a/chapter_linear-networks/linear-regression.md b/chapter_linear-networks/linear-regression.md index 282cc5fdd..b2120cc99 100644 --- a/chapter_linear-networks/linear-regression.md +++ b/chapter_linear-networks/linear-regression.md @@ -57,7 +57,7 @@ $$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 index 5e949aee2..7f360c220 100644 --- 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 index b8aa6e945..cbfc3f03f 100644 --- 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 index f109361e1..58276499d 100644 --- 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 index 17a0bd07f..ac623a56b 100644 --- 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 index 95cad05bf..d5b84c3c8 100644 --- 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 index 6f63ff384..38fbedb55 100644 --- 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 index 51ff963e1..6848c7f0f 100644 --- 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 index de831076a..6535308d5 100644 --- 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 index 0e567e5d5..bdfb834ea 100644 --- 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` diff --git a/img/attention_output.svg b/img/attention_output.svg deleted file mode 100644 index c5731a4a9..000000000 --- a/img/attention_output.svg +++ /dev/null @@ -1,250 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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