Update Notes (from Karaf 3.x to 4.x versions) 3.1. Distributions 3.2. Commands 3.3. Features repositories 3.4. Features resolver 3.5. Namespaces 3.6. Maven plugin 3.7. Update guide 4. User Guide 4 Feature and resolver 4.10.4. Features repositories 4.10.5. Boot features 4.10.6. Features upgrade 4.10.7. Overrides 4.10.8. Feature bundles 4.10.9. Dependent features 4.10.10. Feature configurations JMX FeatureMBean 4.11. Deployers 4.11.1. Blueprint deployer 4.11.2. Spring deployer 4.11.3. Features deployer 4.11.4. KAR deployer 4.11.5. War deployer 4.11.6. Wrap deployer 4.12. KAR 4.12.1.

Equinox OSGi frameworks, and provide additional features on top of the framework. Apache Karaf can be scaled from a very lightweight container to a fully features enterprise service: it's a very flexible and and extensible container, covering all the major needs. Here is a short list of provided features: • Hot deployment: simply drop a file in the deploy directory, Apache Karaf will detect the type of the feature:repo-add camel 2.10.0 Adding feature url mvn:org.apache.camel.karaf/apache-camel/ 2.10.0/xml/features karaf@root()> feature:install camel-spring karaf@root()> bundle:install -s mvn:org.apache.camel/

Windows 2.4. Building on Unix 3. Deploy Cellar 3.1. Registering Cellar features 3.2. Starting Cellar 3.3. Optional features 4. Core runtime and Hazelcast 4.1. Hazelcast cluster identification 4 resources 7. Cellar groups 7.1. New group 7.2. Clustered Resources and Cluster Groups 7.2.1. Features 7.2.2. Bundles 7.2.3. Configurations 7.2.4. OBR (optional) 7.2.5. EventAdmin (optional) 7.3 hostname or IP and port list). Cellar is able to synchronize: * bundles (remote or local) * config * features Optionally, Cellar also support synchronization of OSGi EventAdmin, OBR (URLs and bundles). The

Windows 2.4. Building on Unix 3. Deploy Cellar 3.1. Registering Cellar features 3.2. Starting Cellar 3.3. Optional features 4. Core runtime and Hazelcast 4.1. Hazelcast cluster identification 4 resources 7. Cellar groups 7.1. New group 7.2. Clustered Resources and Cluster Groups 7.2.1. Features 7.2.2. Bundles 7.2.3. Configurations 7.2.4. OBR (optional) 7.2.5. EventAdmin (optional) 7.3 port list). Cellar is able to synchronize: • bundles (remote, local, or from an OBR) • config • features • eventadmin Optionally, Cellar also support synchronization of OSGi EventAdmin, OBR (URLs and

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()

2 Some terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.3 Features overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.4 Supported Post Installation Configuration . . . . . . . . . . . . . . . . . . . . . . . 220 13.3 Security Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 13.3.1 The Security Model . . . . . . . . . . . . . . . . . . . . . . . 222 14 Known limitations 223 14.1 Experimental Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 14.2 Known Issues . . . .

2 Some terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.3 Features overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.4 Supported Post Installation Configuration . . . . . . . . . . . . . . . . . . . . . . . 217 13.3 Security Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 13.3.1 The Security Model . . . . . . . . . . . . . . . . . . . . . . . 219 14 Known limitations 220 14.1 Experimental Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 14.2 Known Issues . . . .

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

2 Some terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3 Features overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.4 Supported Post Installation Configuration . . . . . . . . . . . . . . . . . . . . . . . 193 13.3 Security Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 13.3.1 The Security Model . . . . . . . . . . . . . . . . . . . . . . . 195 14 Known limitations 196 14.1 Experimental Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 14.2 Known Issues . . . .