examples, see: ‣ PyTorch website ‣ PyTorch project This document provides information about the key features, software enhancements and improvements, known issues, and how to run this container. PyTorch RN-08516-001_v23 details, see Deep Learning Frameworks Support Matrix. Key Features and Enhancements This PyTorch release includes the following key features and enhancements. ‣ PyTorch container image version 23.07 details, see Deep Learning Frameworks Support Matrix. Key Features and Enhancements This PyTorch release includes the following key features and enhancements. ‣ PyTorch container image version 23.06

and identically distributed, i.i.d.)。样本有时也叫做数据点 （data point）或者数据实例（data instance），通常每个样本由一组称为特征（features，或协变量（covariates）） 的属性组成。机器学习模型会根据这些属性进行预测。在上面的监督学习问题中，要预测的是一个特殊的属 性，它被称为标签（label，或目标（target））。 true_b = 4.2 features, labels = synthetic_data(true_w, true_b, 1000) 47 https://discuss.d2l.ai/t/1775 3.2. 线性回归的从零开始实现 95 注意，features中的每一行都包含一个二维数据样本，labels中的每一行都包含一维标签值（一个标量）。 print('features:', features[0] '\nlabel:', labels[0]) features: tensor([1.4632, 0.5511]) label: tensor([5.2498]) 通过生成第二个特征features[:, 1]和labels的散点图，可以直观观察到两者之间的线性关系。 d2l.set_figsize() d2l.plt.scatter(features[:, (1)].detach().numpy()

Convolutional Neural Nets (CNNs) were another important breakthrough that enabled learning spatial features in the input. Recurrent Neural Nets (RNNs) facilitated learning from the sequences and temporal having an algorithmic way to meaningfully represent these inputs using a small number of numerical features, will help us solve tasks related to these inputs. Ideally this representation is such that similar similar representations. We will call this representation an Embedding. An embedding is a vector of features that represent aspects of an input numerically. It must fulfill the following goals: a) To compress

x = x.view(-1, self.num_flat_features(x)) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x def num_flat_features(self, x): size = x.size()[1:] size()[1:] num_features = 1 for s in size: num_features *= s return num_features net = Net() print(net) Net( (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1)) stride=(1, 1)) (fc1): Linear(in_features=400, out_features=120, bias=True) (fc2): Linear(in_features=120, out_features=84, bias=True) (fc3): Linear(in_features=84, out_features=10, bias=True) ) 在模型中必须要定义

表达能力强 网络结构灵活 User features Relation features Contextual features Continueous featues Categorical features normalize one-hot encode embedding one-hot encode Content features ReLU(256) ReLU(128) 深度学习应用实践 —— DeepFM User features Relation features Contextual features Continueous featues Categorical features normalize one-hot encode embedding Content features ReLU(256) ReLU(128) ReLU(64)

........................................................................................... 1 Features ............................................................................................... learning applications. Features Keras leverages various optimization techniques to make high level neural network API easier and more performant. It supports the following features:  Consistent, simple the input of the next subsequent layer. By using this approach, we can process huge amount of features, which makes deep learning a very powerful tool. Deep learning algorithms are also useful for the

layers import Embedding from keras.layers import LSTM model = Sequential() model.add(Embedding(max_features, output_dim=256)) model.add(LSTM(128)) model.add(Dropout(0.5)) model.add(Dense(1, activation='sigmoid')) 整数张量，表示将与输入相乘的二进制 dropout 掩层的形状。例如，如果 你的输入尺寸为 (batch_size, timesteps, features)，然后你希望 dropout 掩层在所有 时间步都是一样的，你可以使用 noise_shape=(batch_size, 1, features)。 • seed: 一个作为随机种子的 Python 整数。 参考文献 • Dropout: A Simple output_shape == (None, 3, 32) 参数 • n: 整数，重复次数。 输入尺寸 2D 张量，尺寸为 (num_samples, features)。 输出尺寸 3D 张量，尺寸为 (num_samples, n, features)。 5.2.9 Lambda [source] keras.layers.Lambda(function, output_shape=None

scratch. For models that share the same domain, it is likely that the first few layers learn similar features. Hence training new models from scratch for these tasks is likely wasteful. Regarding the first data we provided to the model in this fine-tuning stage is not being used for learning rudimentary features, but rather how to map the high-level representations it learned in the pretraining stage to solving emnlp-main.831. 10 OpenAI GPT-3 API https://openai.com/api/ 9 GitHub Copilot: https://github.com/features/copilot import tensorflow_datasets as tfds with tf.device('/job:localhost'): ds = tfds.load('ag_news_subset'

y) β j(y) = �m i=1 1(y(i) = y ∧ x(i) j = x) �m i=1 1(y(i) = y) (23) Remark: We assume binary features (Xj ∈ {0, 1} for ∀j ∈ [n]) in the above discussion. What if Xj ∈ {1, 2, · · · , v}? Can we get similar y)p1(x2 | y) · · · p¯j(¯x | y) · · · pn(xn | y) = 0 for ∀y. It is shown that, even the remaining features all have very “strong” conditional probabilities, p(y | x) is forcibly set to be zero due to only sample may involves a different number of features. We assume that the i-th training sample x(i) has ni features. For ∀i ∈ [m], x(i) has each of its features drawn from a sample space [v] = {1, 2, · ·

cat Some of them may not Whether there is a cat is random An image is represented by a vector of features The feature vectors are random, since the images are randomly given Random variable X representing NB and EM September 27, 2023 35 / 122 Warm Up (Contd.) Suppose we have n features X = [X1, X2, · · · , Xn]T The features are independent with each other P(X = x | Y = y) = P(X1 = x1, · · · , Xn = is a n-dimensional vector Each feature x(i) j ∈ {0, 1} (j = 1, · · · , n) y (i) ∈ {0, 1} The features and labels can be represented by random variables {Xj}j=1,··· ,n and Y , respectively Feng Li (SDU)