uint8) img = cv2.imdecode(img_array, cv2.IMREAD_COLOR) img = cv2.resize(img, (IMG_SIZE, IMG_SIZE), cv2.INTER_AREA) return cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(int) def show_image(image): # Display make better transformation choices. A few other commonly used techniques are contrast augmentation, color correction, hue augmentation, saturation, cutout, etc. Figure 3-7 shows a breakdown of the contributions in "texting" or the use of short message standard (SMS) services such as Twitter. For telegrams, space was at a premium—economically speaking—and abbreviations were used as necessity. This motivation

maintaining legibility and clarity. We use the circular dot as a measurement reference for clear space surrounding the symbol. Please keep at least 1/2 distance of the symbol’s width at all times. Don'ts Leverage the color palettes and keep things simple, ensuring there is a strong contrast between the symbol and the background. Don’t use colors that aren’t in the approved color palette or or primary brand color, please use it sparingly. We prefer to apply PyTorch Orange as a deliberate accent. To achieve the best AA compliance color contrast, PyTorch has a special color palette to best

称为变量（variable），它们表示未知的标量值。 本书采用了数学表示法，其中标量变量由普通小写字母表示（例如，x、y和z）。本书用R表示所有（连续）实 数标量的空间，之后将严格定义空间（space）是什么，但现在只要记住表达式x ∈ R是表示x是一个实值标量 的正式形式。符号∈称为“属于”，它表示“是集合中的成员”。例如x, y ∈ {0, 1}可以用来表明x和y是值只能 为0或1的数字。 plt.plot(estimates[:, i].numpy(), label=("P(die=" + str(i + 1) + ")")) d2l.plt.axhline(y=0.167, color='black', linestyle='dashed') d2l.plt.gca().set_xlabel('Groups of experiments') d2l.plt.gca().set_ylabel('Estimated 验获得更多的数据时，这6条实体曲线向真实概率收敛。 概率论公理 在处理骰子掷出时，我们将集合S = {1, 2, 3, 4, 5, 6} 称为样本空间（sample space）或结果空间（outcome space），其中每个元素都是结果（outcome）。事件（event）是一组给定样本空间的随机结果。例如，“看 到5”（{5}）和“看到奇数”（{1, 3, 5}）都是掷出骰子的有

若要了解更多和 Skipgram 有关的知识，请参阅这份由 Mikolov 等人发表的经典论文：Efficient Estimation of Word Representations in Vector Space 参数 • sequence: 一个编码为单词索引（整数）列表的词序列（句子） 。如果使用一个 sampling_table，词索引应该以一个相关数据集的词的排名匹配（例如，10 将会编码为 flow_from_directory keras.preprocessing.image.flow_from_directory(directory, target_size=(256,256), color_mode='rgb', classes=None, class_mode='categorical', batch_size=32, shuffle=True, seed=None, 图像，都将被包含在生成器中。更多细节，详见 此脚本。 • target_size: 整数元组 (height, width)，默认：(256, 256)。所有的图像将被调整到的尺 寸。 • color_mode: “grayscale”, “rbg” 之一。默认：“rgb”。图像是否被转换成 1 或 3 个颜色通道。 • classes: 可选的类的子目录列表（例如 ['dogs', 'ca

型算法才能取得较好的 泛化性能。 9.1 模型的容量 通俗地讲，模型的容量或表达能力，是指模型拟合复杂函数的能力。一种体现模型容 量的指标为模型的假设空间(Hypothesis Space)大小，即模型可以表示的函数集的大小。假 设空间越大越完备，从假设空间中搜索出逼近真实模型的函数也就越有可能；反之，如果 假设空间非常受限，就很难从中找到逼近真实模型的函数。 考虑采样自真实分布 gca(projection='3d') ax.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0)) ax.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0)) ax.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0)) # 绘制权值矩阵范围 surf 复位游戏，回到初始状态 for _ in range(1000): # 循环交互 1000 次 env.render() # 显示当前时间戳的游戏画面 action = env.action_space.sample() # 随机生成一个动作 # 与环境交互，返回新的状态，奖励，是否结束标志，其他信息 observation, reward, done, info = env.step(action)

538-pixel TIFF image named bird large.tiff. It looks like the picture below. In a straightforward 24-bit color representation of this image, each pixel is represented as three 8-bit numbers (ranging from 0 to K-means to reduce the color count to k = 16. That is, you will compute 16 colors as the cluster centroids and replace each pixel in the image with its nearest cluster centroid color. Because computing cluster the means will be initialized to the same color (i.e. black). Depending on your implementation, all of the pixels in the photo that are closest to that color may get assigned to one of the means, leaving

choices that we face when training a deep learning model in the computer vision domain. A Search Space for n parameters is a n-dimensional region such that a point in such a region is a set of well-defined those parameters. The parameters can take discrete or continuous values. It is called a "search" space because we are searching for a point in which minimizes (or maximizes) an Evaluation Function . Formally example for choosing quantization and/or clustering techniques for model optimization. We have a search space which has two boolean valued parameters: quantization and clustering. A $$True$$ value means that

Margin In a n-dimensional space, a hyper plane is defined by ωT x + b = 0 (1) where ω ∈ Rn is the outward pointing normal vector, and b is the bias term. The n-dimensional space is separated into two half-spaces mapping the data into a higher-dimensional feature space where it exhibits linear patterns, we can employ the linear classification model in the new feature space. 8 Figure 3: Non-linear data v.s. linear classifier x (i.e., the data samples lie in a 1-dimensional space), and no linear separator exists for this data. We map each data sample into a 2-dimensional space by x → {x, x2}, such that each sample now has two

(SMO) Algorithm Feng Li (SDU) SVM December 28, 2021 2 / 82 Hyperplane Separates a n-dimensional space into two half-spaces Defined by an outward pointing normal vector ω ∈ Rn Assumption: The hyperplane data to higher dimensions where it exhibits linear patterns Apply the linear model in the new input space Mapping is equivalent to changing the feature representation Feng Li (SDU) SVM December 28, 2021 when the new space is very high dimensional Storing and using these mappings in later computations can be expensive (e.g., we may have to compute inner products in a very high dimensional space) Using the

the outputs are approximately the same . Such a model is useful if we want to deploy a model in a space constrained environment like a mobile device. To summarize, compression techniques help to achieve error (-5.023 v/s -5.0)? If we can tolerate some loss of precision, can we use b-bits and save some space? Let us work on a scheme for going from this higher-precision domain (32-bits) to a quantized domain to the uint8 data type. Note that b might be less than 8, in which case uint8 leads to unnecessary space wastage. If that is indeed the case, you might have to design your own mechanism to pack in multiple