微软认证480：HTML5、JavaScript与CSS3编程指南

微软认证
需积分: 9 2 下载量 132 浏览量 更新于2024-07-23 收藏 13.97MB PDF 举报
"编程指南——HTML5与JavaScript及CSS3" 本资源是一本由Glenn Johnson编写的编程指南，专门针对微软认证480课程。它旨在帮助学习者掌握使用HTML5、JavaScript和CSS3进行web开发的核心技能。这本书由微软出版社出版，并遵循严格的版权规定，未经许可不得复制或传播。 HTML5是现代网页开发的基础，它引入了许多新特性，如语义化标签（如<header>、<footer>和<section>）、媒体元素（如<audio>和<video>）、离线存储以及拖放功能等。学习HTML5意味着能够创建更结构化、更富媒体的网页，提高用户体验。 JavaScript是一种广泛使用的客户端脚本语言，用于增强网页的交互性。通过JavaScript，开发者可以实现动态内容更新、表单验证、动画效果以及与服务器的异步通信（AJAX）。在本书中，读者将学习如何使用JavaScript来处理DOM（文档对象模型），并实现事件驱动的编程逻辑。 CSS3是CSS的最新版本，提供了许多新的选择器、样式和布局模式。例如，CSS3支持边框圆角、阴影、渐变、动画和过渡效果，以及媒体查询（media queries），这使得开发者能构建响应式网页，适应不同设备的屏幕尺寸。此外，CSS3还引入了Flexbox和Grid布局，以简化页面元素的对齐和排列。 书中的内容可能涵盖以下主题： 1. HTML5基础知识：标记语言的结构，新元素的使用，以及如何正确嵌入多媒体。 2. JavaScript基础：变量、数据类型、控制流、函数、对象和数组。 3. DOM操作：查找、修改和添加HTML元素，以及事件处理。 4. CSS3选择器：了解新的选择器，如类选择器、属性选择器和伪类。 5. CSS3样式：边框、背景、字体、布局和动画效果的设置。 6. 响应式设计：利用媒体查询创建跨设备兼容的网页。 7. AJAX和JSON：异步数据交换和数据格式化。 8. 应用实践：通过实际项目加深对HTML5、JavaScript和CSS3的理解和应用。 此书适合有一定编程基础的学习者，通过阅读和实践，他们将能够有效地利用这些技术来构建现代化的网页应用。微软认证480的学习者可以通过这本书获得必要的知识和技能，以通过相关的认证考试。同时，微软出版社提供技术支持和反馈渠道，鼓励读者分享学习体验。

Training Guide: Programming in HTML5 with JavaScript and CSS3

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本《Training Guide: Programming in HTML5 with JavaScript and CSS3》是为有经验的程序员编写的培训手册，旨在帮助他们开发现实世界中与工作角色相关的技能，主要关注于使用HTML5、JavaScript和CSS3来创建应用程序...

70-480Training Guide_Programming in HTML5 with JavaScript and CSS3

2015-07-11 上传
《70-480 Training Guide：Programming in HTML5 with JavaScript and CSS3》是由Glenn Johnson撰写，由Microsoft Press出版的微软认证考试70-480的学习指南。这本书主要面向那些负责开发或计划开发HTML文档，如网页...

Unifying Training and Inference for Neural Image Generation

2023-05-12 上传
The authors demonstrate the effectiveness of their approach on several benchmark datasets, showing that it achieves state-of-the-art results with fewer parameters and faster training times. The ...

One way to create a multivariate model is to: 1. Rank the independent variables by correlation, then create a linear model using the independent variable with the highest correlation. Measure the training and testing accuracy. 2. Add in the independent variable with the next highest correlation and create a new linear model. Measure the training and testing accuracy. 3. Stop when either accuracy score levels off or goes down.follow the steps above to create models with one, two and three independent variables, printing the training and testing accuracy each time. Note that you have to run _train_test_split_ for each model. Set the _random_state_ parameter in _train_test_split_ to 0 each time.

2023-07-12 上传
You can repeat Step 2 and Step 3 by adding more independent variables to `X` and fitting the linear regression model. Remember to perform the train-test split for each model and set `random_state` to ...

Write a Style transfer program based on convolution neural network with python, and save the training weight in a file separately.

2023-03-30 上传
5. Save the trained model weights in a separate file for future use. Here is a sample code for creating a style transfer program based on convolution neural network with Python: ``` # Import ...

Complete the code for Differentially Private Stochastic Gradient Descent. a. Fill in the code for per-example clipping and adding Gaussian noise. b. Implement the privacy budget composition. Calculate the privacy budget of the training process, which means calculating $\epsilon$ based on the variance of Gaussian noise $\sigma^2$ and the given $\delta = 10^{-5}$ in different epochs. You can use basic composition to complete the code. If you correctly apply the Moments Accountant method, you will receive bonus points. import numpy as np from scipy import optimize from scipy.stats import norm import math """ Optionally you could use moments accountant to implement the epsilon calculation. """ def get_epsilon(epoch, delta, sigma, sensitivity, batch_size, training_nums): """ Compute epsilon with basic composition from given epoch, delta, sigma, sensitivity, batch_size and the number of training set. """ return epsilon

2023-06-12 上传
For each epoch, it loops over the training set in batches and computes the gradient of the loss function using the sigmoid function. It then clips the gradient per example, adds Gaussian noise to the...

# train with base lr in the first 100 epochs # and half the lr in the last 100 epochs

2023-07-16 上传
To train with a base learning rate for the first 100 epochs and half the learning rate for the last 100 epochs, you can use a learning rate scheduler in PyTorch. Here's an example of how you can ...

Your+company+has+planned+three+training+courses:+Foreign+Language+Development,+Changes+in+Technology

2023-11-08 上传
Based on the provided references, I cannot find any information related... This can be achieved through attending training courses, reading industry publications, and networking with other professionals.

Input: f, g, A, X, n epochs, n epoch f t Compute T as presented in Section 3.1. Compute TCF , ACF by Eqs. (3) and (4). // model training for epoch in range(n epochs) do Z = f(A, X). Get bA and bACF via g with Eqs. (6) and (7). Update Θf and Θg with L. (Eq. (11)) end for // decoder fine-tuning Freeze Θf and re-initialize Θg. Z = f(A, X). for epoch in range(n epochs f t) do Get bA via g with Eq. (6). Update Θg with LF . (Eq. (8)) end for // inference Z = f(A, X). Get bA and bACF via g with Eqs. (6) and (7). Output: bA for link prediction, bACF .

2023-05-15 上传
The algorithm involves training a model (f) and a generator (g) using a dataset (A, X) for a specified number of epochs. The code then performs decoder fine-tuning and inference to output bA and bACF...

Given a dataset with only two training samples and . Suppose we want to build a Linear Regression model with learning rate . At first, and . 1)Please calculate the cost . 2)Update the parameters and for one time. 3)Please calculate the new cost after updating.

2023-05-30 上传
- Two training samples: `(2, 3)` and `(4, 5)` - Initial parameters: `theta0 = 0, theta1 = 0` - Learning rate: `alpha = 0.01` To build a Linear Regression model, we need to minimize the cost function....
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