Grinberg's Flask Mega Tutorial: Step-by-Step Guide for Flask Web...

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"The new and improved Flask Mega Tutorial" 是由 Miguel Grinberg 所著的一本深入指南,专为那些想要学习和实践 Python Web 开发,特别是使用 Flask 微框架的学生和开发者设计。这本教程不仅包含了基础知识,如安装 Python 和 Flask,还涵盖了更高级的主题,如模板引擎、Web 表单处理、数据库集成(包括 SQLAlchemy 和 Flask-SQLAlchemy)、用户登录管理以及数据库迁移。 该书以“Hello, World!”示例开始,引导读者逐步了解 Flask 的基本工作原理,包括设置环境、创建简单的应用和运行第一个功能。章节2讨论了模板技术,如何通过条件语句、循环和继承来构建动态页面。模板是 Flask 应用的核心组件,能够将数据与HTML代码分离,提供丰富的可重用布局。 在第3章,作者介绍了 Flask-WTF,一个用于处理Web表单的工具包,涉及配置、表单验证、数据接收和链接生成。表单视图的编写展示了如何在用户界面与后端逻辑之间建立连接。 章节4深入探讨数据库集成,讲解了 Flask 中数据库的使用、SQLAlchemy 进行对象关系映射(ORM)以及迁移数据库结构的能力。作者通过实例展示了如何创建模型、执行迁移、以及处理数据库关系。 第5章重点关注用户身份验证,包括密码哈希和安全存储、Flask-Login 模块的介绍以及如何整合用户模型进行登录功能。这部分内容对于任何需要保护用户数据的应用至关重要。 在整个教程中,作者采用边做边学的方法,确保读者在实践中理解和掌握每项技能。无论是初学者还是有一定经验的开发者,都能从中找到适合自己的内容,提升在 Flask 项目中的开发能力。此外,书中还提及了 Bootstrap,尽管没有详细列出,但可以推测在某些章节可能涉及响应式前端设计的实践。 这是一本全面且实用的 Flask 教程,适合希望通过实际操作加深对 Python Web开发理解的人士,涵盖了从基础到进阶的各个层面,有助于读者快速成长为熟练的 Flask 开发者。

Traditional network security situation prediction methods depend on the accuracy of historical situation value. Moreover, there are differences in correlation and importance among various network security factors. In order to solve these problems, a combined prediction model based on the temporal convolution attention network (TCAN) and bi-directional gate recurrent unit (BiGRU) network optimized by singular spectrum analysis (SSA) and improved quantum particle swarm optimization algorithm (IQPSO) was proposed. This model was first decomposed and reconstructed into a series of subsequences through the SSA of network security situation data. Next, a prediction model of TCAN-BiGRU was established for each subsequence, respectively. The TCN with relatively simple structure was used in the TCAN to extract features from the data. Besides, the improved channel attention mechanism (CAM) was used to extract important feature information from TCN. Afterwards, the before-after status of the learning situation value of the BiGRU neural network was used to extract more feature information from sequences for prediction. Meanwhile, an improved IQPSO was proposed to optimize the hyper-parameter of the BiGRU neural network. Finally, the prediction results of subsequence were superimposed to obtain the final predicted value. In the experiment, on the one hand, the IQPSO was compared with other optimization algorithms; and the results showed that the IQPSO has better optimization performance; on the other hand, the comparison with traditional prediction methods was performed through the simulation experiment and the established prediction model; and the results showed that the combined prediction model established has higher prediction accuracy.

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